----------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  D:\Dropbox\2016 Japan project\Japan Main Survey\replication\replication.log
  log type:  text
 opened on:  16 Feb 2022, 13:26:38

. do "D:\Dropbox\2016 Japan project\Japan Main Survey\replication\replication code.do"

. 
. ******************************************************************************
. *                                               REPLICATION CODES 
. *                                                               for 
. *  Belief in Territorial Indivisibility & Public Preferences for Dispute Resolution
. *                                               Version: Feb 12, 2022
. ******************************************************************************
. 
. **Make sure that the raw data "japan_raw.dta" is in the same folder as the do file
. **Make sure that the file "prefgeo.dta" is in the same folder as the do file
. 
. **install package "rowranks"
. **ssc install rowranks
. 
. use "japan_raw.dta"

. 
. 
. **variable construction
. 
. //socioeconomic
. 
. gen age = 2016 - b01 

. label variable age "Age"

. 
. gen male = 2 - b03

. label variable  male "Male"

. 
. gen edu=b06

. label variable edu "Education Level"

. 
. gen edu1=0

. replace edu1=1 if b06 <= 2
(599 real changes made)

. label variable edu1 "No College"

. 
. gen edu2=0

. replace edu2=1 if b06==3 | b06==4
(1,771 real changes made)

. label variable edu2 "College Education"

. 
. gen edu3=0

. replace edu3=1 if b06 > = 5
(251 real changes made)

. label variable edu3 "Postgraduate Education"

. 
. gen college = 0 

. replace college = 1 if b06 >= 4
(1,490 real changes made)

. label variable college "College Degree"

. 
. gen fulltime = 0

. replace fulltime = 1 if b07 <= 2
(1,193 real changes made)

. label variable fulltime "Full-time Job"

. 
. gen parttime = 0

. replace parttime = 1 if b07 == 3 | b07 == 4
(390 real changes made)

. label variable parttime "Part-time Job"

. 
. 
. gen selfemp = 0

. replace selfemp = 1 if b07 == 5
(240 real changes made)

. 
. gen unemp = 0

. replace unemp = 1 if b07 >= 6
(798 real changes made)

. 
. gen income=b09
(10 missing values generated)

. label variable income "Income"

. 
. destring b10, gen(socialstatus)
b10: all characters numeric; socialstatus generated as byte

. label variable socialstatus "Social Status"

. 
. gen news = 5 - b13

. label variable news "Interest in International Affairs"

. 
. destring b15x_1 b15x_2 b15x_3 b15x_4 b15x_5 b15x_6 b15x_7 , ///
> gen(b15_1 b15_2 b15_3 b15_4 b15_5 b15_6 b15_7 )
b15x_1: all characters numeric; b15_1 generated as byte
b15x_2: all characters numeric; b15_2 generated as byte
b15x_3: all characters numeric; b15_3 generated as byte
b15x_4: all characters numeric; b15_4 generated as byte
b15x_5: all characters numeric; b15_5 generated as byte
b15x_6: all characters numeric; b15_6 generated as byte
b15x_7: all characters numeric; b15_7 generated as byte

. 
. rowranks b15_1 b15_2 b15_3 b15_4 b15_5 b15_6 b15_7, gen(r1-r7) highrank

. 
. gen defense=0

. replace defense=1 if r2==7
(1,005 real changes made)

. gen rank_defense=r2

. label variable defense "National Defense Top Issue"

. label variable rank_defense "Ranking of National Defense among All Issues"

. 
. gen nationalism1 = 0

. replace nationalism1 = 1 if b16_1 < 3
(2,131 real changes made)

. 
. gen nationalism2 = 0

. replace nationalism2 = 1 if b16_2 < 3
(1,965 real changes made)

. 
. gen nationalism3 = 0

. replace nationalism3 = 1 if b16_3 < 3
(1,264 real changes made)

. 
. gen nationalism4 = 0

. replace nationalism4 = 1 if b16_4 < 3
(1,997 real changes made)

. 
. gen nationalism5 = 0

. replace nationalism5 = 1 if b16_5 < 3
(766 real changes made)

. 
. gen nationalism = (nationalism1 + nationalism2 + nationalism3 + nationalism4 + nationalism5) / 5 

. label variable nationalism "Nationalism"

. 
. gen ldp=0

. replace ldp=1 if b11==1
(669 real changes made)

. label variable ldp "Liberal Democratic Party"

. 
. gen noparty=0

. replace noparty=1 if b11==8
(1,281 real changes made)

. label variable noparty "No Political Party"

. 
. destring b12, gen(conserv)
b12: all characters numeric; conserv generated as byte

. replace conserv = . if conserv == 11
(250 real changes made, 250 to missing)

. label variable conserv "Conservatism"

. 
. **countries in mind (duplicate)
. gen dup = a14x_china+a14x_russia+a14x_south       

. 
. **Regions
. ren b04 q09

. destring q09, generate(q09a)
q09: all characters numeric; q09a generated as byte

. generate hokkaidotohoku=0

. label var hokkaidotohoku "Hokkaido&Tohoku"

. replace hokkaidotohoku=1 if q09a==01
(152 real changes made)

. replace hokkaidotohoku=1 if q09a==02
(22 real changes made)

. replace hokkaidotohoku=1 if q09a==03
(21 real changes made)

. replace hokkaidotohoku=1 if q09a==04
(53 real changes made)

. replace hokkaidotohoku=1 if q09a==05
(14 real changes made)

. replace hokkaidotohoku=1 if q09a==06
(23 real changes made)

. replace hokkaidotohoku=1 if q09a==07
(23 real changes made)

. generate kantokoshinetsu=0

. label var kantokoshinetsu "Kanto&Koshinetsu"

. replace kantokoshinetsu=1 if q09a==08
(24 real changes made)

. replace kantokoshinetsu=1 if q09a==09
(24 real changes made)

. replace kantokoshinetsu=1 if q09a==10
(19 real changes made)

. replace kantokoshinetsu=1 if q09a==11
(131 real changes made)

. replace kantokoshinetsu=1 if q09a==12
(121 real changes made)

. replace kantokoshinetsu=1 if q09a==13
(354 real changes made)

. replace kantokoshinetsu=1 if q09a==14
(194 real changes made)

. replace kantokoshinetsu=1 if q09a==15
(48 real changes made)

. replace kantokoshinetsu=1 if q09a==19
(6 real changes made)

. replace kantokoshinetsu=1 if q09a==20
(38 real changes made)

. generate chubu=0

. label var chubu "Chubu"

. replace chubu=1 if q09a==16
(14 real changes made)

. replace chubu=1 if q09a==17
(22 real changes made)

. replace chubu=1 if q09a==18
(10 real changes made)

. replace chubu=1 if q09a==21
(38 real changes made)

. replace chubu=1 if q09a==22
(80 real changes made)

. replace chubu=1 if q09a==23
(195 real changes made)

. replace chubu=1 if q09a==24
(33 real changes made)

. generate kinki=0

. label var kinki "Kinki"

. replace kinki=1 if q09a==25
(15 real changes made)

. replace kinki=1 if q09a==26
(48 real changes made)

. replace kinki=1 if q09a==27
(191 real changes made)

. replace kinki=1 if q09a==28
(130 real changes made)

. replace kinki=1 if q09a==29
(43 real changes made)

. replace kinki=1 if q09a==30
(11 real changes made)

. generate chugokushikoku=0

. label var chugokushikoku "Chugoku&Shikoku"

. replace chugokushikoku=1 if q09a==31
(10 real changes made)

. replace chugokushikoku=1 if q09a==32
(9 real changes made)

. replace chugokushikoku=1 if q09a==33
(46 real changes made)

. replace chugokushikoku=1 if q09a==34
(66 real changes made)

. replace chugokushikoku=1 if q09a==35
(20 real changes made)

. replace chugokushikoku=1 if q09a==36
(11 real changes made)

. replace chugokushikoku=1 if q09a==37
(31 real changes made)

. replace chugokushikoku=1 if q09a==38
(26 real changes made)

. replace chugokushikoku=1 if q09a==39
(10 real changes made)

. generate kusyuokinawa=0

. label var kusyuokinawa "Kyusyu&Okinawa"

. replace kusyuokinawa=1 if q09a>=40
(295 real changes made)

. gen region=0

. label var region "Region"

. replace region=1 if kantokoshinetsu==1
(959 real changes made)

. replace region=2 if chubu==1
(392 real changes made)

. replace region=3 if kinki==1
(438 real changes made)

. replace region=4 if chugokushikoku==1
(229 real changes made)

. replace region=5 if kusyuokinawa==1
(295 real changes made)

. label define regionlabel ///
>             0 "Hokkaido&Tohoku" ///
>             1 "Kanto&Koshinetsu" ///
>             2 "Chubu" ///
>             3 "Kinki" ///
>             4 "Chugoku&Shikoku" ///
>             5 "Kyusyu&Okinawa"

. label values region "regionlabel"

. 
. ******************************
. // Geographic proximity to disputed areas
. 
. // 1: Prefecture dummies
. //    Takeshima (32 Shimane prefecture)
. //    Senkaku   (47 Okinawa prefecture)
. //    Northern Territory (1 Hokkaido prefecture)
. 
. gen disp_proxim = (q09a == 1 | q09a == 32 | q09a == 47)

. 
. // 2: Minimum distance to disputed areas
. //    See geodata/geodata.R for details about coding
. 
. merge m:1 q09a using "prefgeo.dta"
(note: variable q09a was byte, now long to accommodate using data's values)

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                             2,621  (_merge==3)
    -----------------------------------------

. drop _merge

. 
. label var mindist "Minimum distance to disputed areas"

. label var mindist_std "Minimum distance to disputed areas (standardized)"

. label var mindist_nor "Minimum distance to disputed areas (normalized)"

. 
. ttest mindist_nor, by(disp_proxim)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,445    .6358025    .0053407    .2640837    .6253296    .6462754
       1 |     176    .2792552    .0051712    .0686033    .2690493    .2894611
---------+--------------------------------------------------------------------
combined |   2,621    .6118604    .0052896    .2708063    .6014881    .6222327
---------+--------------------------------------------------------------------
    diff |            .3565473    .0199576                 .317413    .3956816
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  17.8652
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. bysort disp_proxim: sum mindist_nor

----------------------------------------------------------------------------------------------------------------------------------
-> disp_proxim = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 mindist_nor |      2,445    .6358025    .2640837   .1159949          1

----------------------------------------------------------------------------------------------------------------------------------
-> disp_proxim = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 mindist_nor |        176    .2792552    .0686033          0   .3656757


. 
. label variable mindist_nor "Distance"

. ******
. 
. //contextual treatments
. 
. gen powerful = 0

. replace powerful = 1 if inlist(pat, "01", "02", "03", "04", "05", "06")
(1,335 real changes made)

. label variable powerful "Strong Neighbor"

. 
. gen valuable = 0

. replace valuable = 1 if inlist(pat, "01", "02", "03", "07", "08", "09")
(1,342 real changes made)

. label variable valuable "Valuable"

. 
. gen hist_none=0

. replace hist_none=1 if inlist(pat, "03", "06", "09", "12")
(845 real changes made)

. label variable hist_none "Historically Unoccupied"

. 
. gen hist_japan=0

. replace hist_japan=1 if inlist(pat, "01", "04", "07", "10")
(864 real changes made)

. label variable hist_japan "Historically Japanese"

. 
. gen hist_foreign=0

. replace hist_foreign=1 if inlist(pat, "02", "05", "08", "11")
(912 real changes made)

. label variable hist_foreign "Historically Foreign"

. 
. gen io = 0 

. replace io = 1 if a03b != .
(1,351 real changes made)

. 
. 
. //sharing sovereignty and right of use
. replace a01 = 0 if a01 == 2 
(1,311 real changes made)

. label variable a01 "Sharing"

. 
. //Japan sovereignty and right of use
. replace a04 = 0 if a04 == 2 
(636 real changes made)

. label variable a04 "Japan Exclusive"

. 
. //Japan sovereignty and co-development
. replace a02 = 0 if a02 == 2 
(1,077 real changes made)

. label variable a02 "Japan Sovereignty Only"

. 
. //Sidepayment with IO
. replace a03b = 0 if a03b == 2 
(464 real changes made)

. label variable a03b "Sidepayment with IO"

. 
. //Sidepayment without IO
. replace a03a = 0 if a03a == 2
(434 real changes made)

. label variable a03a "Sidepayment without IO"

. 
. 
. //combining sidepayment
. gen a03ab = a03a
(1,351 missing values generated)

. replace a03ab = a03b if a03ab == .
(1,351 real changes made)

. 
. label variable a03ab "Sidepayment"

. 
. 
. 
. //Policy outcomes
. 
. gen q100_1 = 2 - a05a

. label variable q100_1 "Publicity"

. 
. gen q100_2 = 2 - a06

. label variable q100_2 "Sanction"

. 
. gen q100_3 = 2 - a07a

. label variable q100_3 "Limited Military"

. 
. gen q100_4 = 2 - a07b

. label variable q100_4 "Full Military"

. 
. gen q100_5 = 2 - a08a

. label variable q100_5 "Bilateral"

. 
. gen q100_6 = 2 - a09a

. label variable q100_6 "IO Arbitration"

. 
. gen q100_7 = 2 - a10

. label variable q100_7 "Shelving"

. 
. //hardcore
. gen hardcore = 0 

. replace hardcore = 1 if a04 == 1 & a01 == 0 & a02 == 0 & a03ab == 0
(381 real changes made)

. replace hardcore = . if a04 == 3 & a01 == 3 & a02 == 3 & (a03a == 3 | a03b == 3)
(232 real changes made, 232 to missing)

. label variable hardcore "Indivisibility"

. 
. //softie
. gen softie = 0

. replace softie = 1 if a04 == 1 & a01 == 1 & a02 == 1 & a03ab == 1
(207 real changes made)

. replace softie = . if a04 == 3 & a01 == 3 & a02 == 3 & (a03a == 3 | a03b == 3)
(232 real changes made, 232 to missing)

. 
. 
. //continuous measure of attitude toward indivisibility
. 
. *gen hardcore2=hardcore
. *replace hardcore2 = 2 if q103_6 == 1 & q103_1 == 0 & q103_9 == 0 & (q103_10 == 1 | q103_11 == 1)
. *replace hardcore2 = 3 if q103_6 == 1 & q103_1 == 0 & q103_9 == 1 & (q103_10 == 1 | q103_11 == 1)
. *replace hardcore2 = 4 if q103_6 == 1 & q103_1 == 1 & q103_9 == 1 & (q103_10 == 1 | q103_11 == 1)
. 
. *gen hardcore3=0
. *replace hardcore3=q103_6*1+q103_12*2+q103_9*3+q103_1*4
. *replace hardcore3=. if hardcore3==0
. 
. **IRT measure for hardcore
. gen a1=a01

. gen a2=a02

. gen a3=a03ab

. gen a4=a04

. 
. recode a1-a4 (3=.)
(a1: 534 changes made)
(a2: 572 changes made)
(a3: 612 changes made)
(a4: 687 changes made)

. 
. irt 2pl a1-a4, intpoints(5)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -5406.9427  
Iteration 1:   log likelihood =  -5400.966  
Iteration 2:   log likelihood = -5400.9639  
Iteration 3:   log likelihood = -5400.9639  

Fitting full model:

Iteration 0:   log likelihood = -5138.6265  
Iteration 1:   log likelihood =  -4984.204  (not concave)
Iteration 2:   log likelihood = -4898.9926  
Iteration 3:   log likelihood =  -4896.181  
Iteration 4:   log likelihood = -4888.9466  
Iteration 5:   log likelihood =  -4881.138  
Iteration 6:   log likelihood = -4880.6232  
Iteration 7:   log likelihood = -4880.6247  
Iteration 8:   log likelihood = -4880.6205  
Iteration 9:   log likelihood = -4880.6212  
Iteration 10:  log likelihood = -4880.6211  

Two-parameter logistic model                    Number of obs     =      2,389
Log likelihood = -4880.6211
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
a1           |
     Discrim |   2.817334   .2685254    10.49   0.000     2.291034    3.343634
        Diff |   .3735709   .0360743    10.36   0.000     .3028664    .4442753
-------------+----------------------------------------------------------------
a2           |
     Discrim |   3.250783   .3475421     9.35   0.000     2.569613    3.931953
        Diff |    .074559   .0338379     2.20   0.028     .0082378    .1408801
-------------+----------------------------------------------------------------
a3           |
     Discrim |   1.489956    .109514    13.61   0.000     1.275313      1.7046
        Diff |  -.1987807   .0442158    -4.50   0.000    -.2854421   -.1121193
-------------+----------------------------------------------------------------
a4           |
     Discrim |  -.3204631   .0661117    -4.85   0.000    -.4500397   -.1908866
        Diff |   2.238323    .474273     4.72   0.000     1.308765    3.167881
------------------------------------------------------------------------------

. estat report, byparm sort(b)

Two-parameter logistic model                    Number of obs     =      2,389
Log likelihood = -4880.6211
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Discrim      |
          a3 |   1.489956    .109514    13.61   0.000     1.275313      1.7046
          a2 |   3.250783   .3475421     9.35   0.000     2.569613    3.931953
          a1 |   2.817334   .2685254    10.49   0.000     2.291034    3.343634
          a4 |  -.3204631   .0661117    -4.85   0.000    -.4500397   -.1908866
-------------+----------------------------------------------------------------
Diff         |
          a3 |  -.1987807   .0442158    -4.50   0.000    -.2854421   -.1121193
          a2 |    .074559   .0338379     2.20   0.028     .0082378    .1408801
          a1 |   .3735709   .0360743    10.36   0.000     .3028664    .4442753
          a4 |   2.238323    .474273     4.72   0.000     1.308765    3.167881
------------------------------------------------------------------------------

. *irtgraph icc, blocation legend(pos(3) col(1) ring(1) size(small)) xlabel(, alt)
. *irtgraph tcc, thetalines(-1.96 0 1.96)
. predict hardcore4, latent
(option ebmeans assumed)
(using 5 quadrature points)

. replace hardcore4=-hardcore4
(2,389 real changes made)

. label variable hardcore4 "Indivisibility"

. 
. 
. **attention check
. 
. gen check1 = 0

. replace check1 = 1 if a05b == 2
(2,425 real changes made)

. 
. gen check2 = 0

. replace check2 = 1 if a08b == 3
(2,477 real changes made)

. 
. 
. ***Figure 2: Testing H1 with the treatment of historical ownership ("unsure" answers removed)
. 
. mean a01 if a01 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        694

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |   .2478386   .0164011      .2156368    .2800405
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a01 if a01 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        742

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |   .4177898    .018118      .3822211    .4533584
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a01 if a01 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        651

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |   .4516129   .0195196      .4132839    .4899419
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        661

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7594554   .0166371      .7267874    .7921233
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat4 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        670

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .5895522   .0190185       .552209    .6268954
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat5 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        603

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .6650083   .0192368      .6272289    .7027876
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat6 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        689

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |    .467344   .0190216      .4299966    .5046914
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat7 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        707

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |    .466761   .0187761      .4298972    .5036247
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat8 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        653

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |   .4900459   .0195776      .4516031    .5284888
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat9 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        349

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |    .504298   .0268018      .4515841    .5570119
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat10 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        365

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |   .5835616   .0258385      .5327501    .6343732
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat11 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_none == 1

Mean estimation                   Number of obs   =        328

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |   .5762195   .0273269      .5224608    .6299782
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat12 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        320

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |      .5375   .0279158      .4825777    .5924223
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat13 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        335

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |   .5761194   .0270399      .5229295    .6293093
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat14 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_none == 1

Mean estimation                   Number of obs   =        312

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |   .5384615   .0282684      .4828401     .594083
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat15 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. 
. matrix t1 = mat1\mat7\mat10\mat13\mat4

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat8\mat11\mat14\mat5

. matrix rownames t2 = a c d e b

. 
. matrix t3 = mat3\mat9\mat12\mat15\mat6

. matrix rownames t3 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Historically Owned by Japan) m(Oh)) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Historically Owned by Foreign) m(Sh))  ///
>  (matrix(t3[.,1]), ci("t3[.,2] t3[.,3]") label(Historically Owned by Neither) m(Th)),  ///
>  coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6))  ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. graph export figure2.pdf, replace
(file figure2.pdf written in PDF format)

. 
. 
. **Appendix Figure 1: looking at military strength of the neighboring country
. 
. ttest a01 if a01 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,006    .3677932    .0152107    .4824445    .3379449    .3976416
       1 |   1,081    .3755782    .0147359     .484496    .3466639    .4044924
---------+--------------------------------------------------------------------
combined |   2,087    .3718256    .0105816     .483408    .3510739    .3925772
---------+--------------------------------------------------------------------
    diff |           -.0077849    .0211813               -.0493237    .0337538
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.3675
Ho: diff = 0                                     degrees of freedom =     2085

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3566         Pr(|T| > |t|) = 0.7133          Pr(T > t) = 0.6434

. matrix mat1 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat2 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a04 if a04 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     932    .6351931    .0157765    .4816344    .6042316    .6661547
       1 |   1,002    .7045908    .0144199    .4564543     .676294    .7328876
---------+--------------------------------------------------------------------
combined |   1,934    .6711479    .0106855    .4699176    .6501916    .6921041
---------+--------------------------------------------------------------------
    diff |           -.0693977    .0213321               -.1112341   -.0275613
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.2532
Ho: diff = 0                                     degrees of freedom =     1932

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0006         Pr(|T| > |t|) = 0.0012          Pr(T > t) = 0.9994

. matrix mat3 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat4 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a02 if a02 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     995    .4653266    .0158209    .4990471    .4342805    .4963727
       1 |   1,054    .4829222    .0153993    .4999455    .4527053    .5131391
---------+--------------------------------------------------------------------
combined |   2,049    .4743777     .011034     .499465    .4527387    .4960168
---------+--------------------------------------------------------------------
    diff |           -.0175956    .0220792               -.0608956    .0257044
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.7969
Ho: diff = 0                                     degrees of freedom =     2047

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2128         Pr(|T| > |t|) = 0.4256          Pr(T > t) = 0.7872

. matrix mat5 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat6 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03b if a03b < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     511    .5596869    .0219821    .4969111    .5165004    .6028734
       1 |     531    .5499058    .0216102    .4979723    .5074538    .5923579
---------+--------------------------------------------------------------------
combined |   1,042    .5547025    .0154039    .4972373    .5244763    .5849287
---------+--------------------------------------------------------------------
    diff |            .0097811    .0308267               -.0507086    .0702707
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.3173
Ho: diff = 0                                     degrees of freedom =     1040

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6245         Pr(|T| > |t|) = 0.7511          Pr(T > t) = 0.3755

. matrix mat7 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat8 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03a if a03a < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     480    .5604167    .0226782    .4968542    .5158556    .6049777
       1 |     487    .5420945    .0225999    .4987372    .4976888    .5865001
---------+--------------------------------------------------------------------
combined |     967    .5511892    .0160027    .4976301    .5197852    .5825933
---------+--------------------------------------------------------------------
    diff |            .0183222    .0320174               -.0445095     .081154
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.5723
Ho: diff = 0                                     degrees of freedom =      965

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7164         Pr(|T| > |t|) = 0.5673          Pr(T > t) = 0.2836

. matrix mat9 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat10 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. matrix t1 = mat1\mat5\mat7\mat9\mat3

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat6\mat8\mat10\mat4

. matrix rownames t2 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Militarily Weak) m(Oh) ) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Militarily Strong) m(Sh)),  ///
> coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6)) ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. 
. graph export figureA1.pdf, replace
(file figureA1.pdf written in PDF format)

. 
. 
. **Appendix Figure 2: looking at value of the territory
. 
. ttest a01 if a01 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,014    .3658777    .0151339    .4819131    .3361804     .395575
       1 |   1,073    .3774464    .0148053     .484974    .3483957    .4064972
---------+--------------------------------------------------------------------
combined |   2,087    .3718256    .0105816     .483408    .3510739    .3925772
---------+--------------------------------------------------------------------
    diff |           -.0115687    .0211753               -.0530956    .0299582
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.5463
Ho: diff = 0                                     degrees of freedom =     2085

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2924         Pr(|T| > |t|) = 0.5849          Pr(T > t) = 0.7076

. matrix mat1 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat2 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a04 if a04 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     937    .6766275    .0152893    .4680133    .6466222    .7066329
       1 |     997     .665998    .0149445    .4718771    .6366717    .6953243
---------+--------------------------------------------------------------------
combined |   1,934    .6711479    .0106855    .4699176    .6501916    .6921041
---------+--------------------------------------------------------------------
    diff |            .0106295    .0213854               -.0313113    .0525704
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.4970
Ho: diff = 0                                     degrees of freedom =     1932

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6904         Pr(|T| > |t|) = 0.6192          Pr(T > t) = 0.3096

. matrix mat3 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat4 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a02 if a02 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,008    .4672619    .0157225    .4991747    .4364092    .4981146
       1 |   1,041     .481268    .0154935    .4998891     .450866      .51167
---------+--------------------------------------------------------------------
combined |   2,049    .4743777     .011034     .499465    .4527387    .4960168
---------+--------------------------------------------------------------------
    diff |           -.0140061    .0220741               -.0572962     .029284
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6345
Ho: diff = 0                                     degrees of freedom =     2047

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2629         Pr(|T| > |t|) = 0.5258          Pr(T > t) = 0.7371

. matrix mat5 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat6 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03b if a03b < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     497    .5392354    .0223814    .4989604    .4952613    .5832095
       1 |     545    .5688073    .0212334    .4956979    .5270979    .6105168
---------+--------------------------------------------------------------------
combined |   1,042    .5547025    .0154039    .4972373    .5244763    .5849287
---------+--------------------------------------------------------------------
    diff |           -.0295719    .0308417               -.0900909    .0309471
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.9588
Ho: diff = 0                                     degrees of freedom =     1040

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1689         Pr(|T| > |t|) = 0.3379          Pr(T > t) = 0.8311

. matrix mat7 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat8 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03a if a03a < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     487    .5256674    .0226506    .4998542    .4811622    .5701725
       1 |     480    .5770833    .0225725    .4945378    .5327301    .6214366
---------+--------------------------------------------------------------------
combined |     967    .5511892    .0160027    .4976301    .5197852    .5825933
---------+--------------------------------------------------------------------
    diff |            -.051416      .03198               -.1141744    .0113424
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.6078
Ho: diff = 0                                     degrees of freedom =      965

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0541         Pr(|T| > |t|) = 0.1082          Pr(T > t) = 0.9459

. matrix mat9 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat10 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. matrix t1 = mat1\mat5\mat7\mat9\mat3

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat6\mat8\mat10\mat4

. matrix rownames t2 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Value Unsure) m(Oh) ) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Valuable) m(Sh)),  ///
>  coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6)) ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. 
. graph export figureA2.pdf, replace
(file figureA2.pdf written in PDF format)

. 
. //Figure 3: Proportion of Hardcore Indivisible by Contextual Variable
. 
. mean hardcore if hist_japan == 1

Mean estimation                   Number of obs   =        791

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .2073325   .0144233      .1790199    .2356451
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if hist_foreign == 1

Mean estimation                   Number of obs   =        837

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1278375   .0115485      .1051701    .1505049
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if hist_none == 1

Mean estimation                   Number of obs   =        761

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1445466   .0127554      .1195066    .1695867
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if powerful == 1

Mean estimation                   Number of obs   =      1,229

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1635476   .0105546      .1428405    .1842547
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat4 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if powerful == 0

Mean estimation                   Number of obs   =      1,160

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1551724   .0106353      .1343058     .176039
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat5 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if valuable == 1

Mean estimation                   Number of obs   =      1,217

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1487264   .0102038      .1287074    .1687454
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat6 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean hardcore if valuable == 0

Mean estimation                   Number of obs   =      1,172

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    hardcore |   .1706485   .0109937       .149079    .1922179
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat7 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. matrix t1 = mat1\mat2\mat3\mat4\mat5\mat6\mat7

. matrix rownames t1 = a b c d e f g

. 
. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]")),  ///
>  coeflabel(a = `" "Historically" "owned" "by Japan" "' ///
>                    b = `" "Historically" "owned" "by Foreign" "' ///
>            c = `" "Historically" "owned" "by Neither" "' ///
>            d = `" "Strong" "Neighbor" "' ///
>            e = `" "Weak" "Neighbor" "'  ///
>                    f = `" Valuable "'  ///
>                    g = `" "Value" "Unsure" "', labsize(vsmall) ) ///
> mlabel format(%9.3g) mlabposition(1)   ///
> ytitle(Proportion of Hardcore Respondents) vertical scheme(plotplain)

. 
. graph export figure3.pdf, replace
(file figure3.pdf written in PDF format)

. 
. //Figure 4 and Table 9 in Appendix I (abridged Table 1 in main text):
. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_5 > -1

Iteration 0:   log likelihood =  -1014.997  
Iteration 1:   log likelihood = -901.35185  
Iteration 2:   log likelihood = -895.51405  
Iteration 3:   log likelihood = -895.50284  
Iteration 4:   log likelihood = -895.50284  

Logistic regression                             Number of obs     =      1,842
                                                LR chi2(18)       =     238.99
                                                Prob > chi2       =     0.0000
Log likelihood = -895.50284                     Pseudo R2         =     0.1177

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.733409   .1440856   -12.03   0.000    -2.015811   -1.451006
  hist_japan |  -.3850007   .1458543    -2.64   0.008    -.6708699   -.0991315
hist_foreign |  -.0492486   .1490022    -0.33   0.741    -.3412875    .2427903
 nationalism |   .1576502   .2193071     0.72   0.472    -.2721838    .5874842
    powerful |   .0031082   .1185555     0.03   0.979    -.2292564    .2354728
    valuable |  -.2070175   .1191655    -1.74   0.082    -.4405776    .0265427
         age |   .0155654   .0047743     3.26   0.001      .006208    .0249227
        male |  -.2375342   .1366641    -1.74   0.082    -.5053909    .0303226
     college |   .1903725   .1326472     1.44   0.151    -.0696113    .4503563
    fulltime |    -.20963   .1449347    -1.45   0.148    -.4936968    .0744368
    parttime |  -.0921893   .1901175    -0.48   0.628    -.4648127    .2804342
      income |   .0704695   .0461698     1.53   0.127    -.0200215    .1609606
socialstatus |  -.0010204   .0383677    -0.03   0.979    -.0762198    .0741789
        news |   .1991829   .0922028     2.16   0.031     .0184687    .3798971
     defense |  -.3642013   .1241708    -2.93   0.003    -.6075716    -.120831
         ldp |  -.3924684   .1748587    -2.24   0.025    -.7351851   -.0497517
     noparty |  -.2550589   .1535672    -1.66   0.097    -.5560451    .0459274
     conserv |  -.0532218   .0321804    -1.65   0.098    -.1162943    .0098506
       _cons |   .9296974   .4254189     2.19   0.029     .0958917    1.763503
------------------------------------------------------------------------------

. estimates store o5

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA9.tex
tableA9.xml
dir : seeout

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_5

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore   -0.3040   -0.3040   -0.1800   -0.0630   -0.1719

              0       1
Pr(y|x)  0.1116  0.8884

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            47             1
sd_x=       .364312       .469776       .476289       .284815        .50006       .499905       13.6169       .495206

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .488201       .499936       .346019       1.51407       1.82399       .678214       .479953       .452828

            noparty       conserv
   x=             0             5
sd_x=       .497059       2.05301

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_5

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.8868   [ 0.8440,    0.9295]
  Pr(y=0|x):          0.1132   [ 0.0705,    0.1560]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .60000002             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat9 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_5

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.4935   [ 0.3812,    0.6057]
  Pr(y=0|x):          0.5065   [ 0.3943,    0.6188]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             1             0             3             5             3             1             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat10 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_7 > -1

Iteration 0:   log likelihood = -1122.0834  
Iteration 1:   log likelihood = -1014.9878  
Iteration 2:   log likelihood = -1011.9505  
Iteration 3:   log likelihood = -1011.9427  
Iteration 4:   log likelihood = -1011.9427  

Logistic regression                             Number of obs     =      1,694
                                                LR chi2(18)       =     220.28
                                                Prob > chi2       =     0.0000
Log likelihood = -1011.9427                     Pseudo R2         =     0.0982

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.619315   .1931012    -8.39   0.000    -1.997786   -1.240843
  hist_japan |  -.7609177   .1337141    -5.69   0.000    -1.022993   -.4988428
hist_foreign |   -.332267   .1292733    -2.57   0.010     -.585638    -.078896
 nationalism |  -.2844489   .1966218    -1.45   0.148    -.6698207    .1009228
    powerful |  -.0470704   .1073303    -0.44   0.661     -.257434    .1632931
    valuable |  -.0467733   .1077279    -0.43   0.664     -.257916    .1643695
         age |   .0083538   .0042753     1.95   0.051    -.0000257    .0167333
        male |   .0206109   .1244909     0.17   0.869    -.2233868    .2646085
     college |   .2228957   .1194585     1.87   0.062    -.0112387    .4570302
    fulltime |   .0574525   .1295454     0.44   0.657    -.1964519    .3113569
    parttime |   .1187413   .1716731     0.69   0.489    -.2177318    .4552145
      income |  -.0606975   .0421458    -1.44   0.150    -.1433017    .0219067
socialstatus |    .099291   .0348491     2.85   0.004      .030988    .1675939
        news |   .1119486   .0866097     1.29   0.196    -.0578032    .2817004
     defense |  -.4946763   .1164507    -4.25   0.000    -.7229155   -.2664371
         ldp |  -.3638829    .156993    -2.32   0.020    -.6715835   -.0561823
     noparty |  -.2537515   .1314823    -1.93   0.054    -.5114521    .0039491
     conserv |  -.0852706   .0289025    -2.95   0.003    -.1419186   -.0286227
       _cons |  -.0931173   .4024361    -0.23   0.817    -.8818777     .695643
------------------------------------------------------------------------------

. estimates store o7

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. outtex, lab detail level below title(Dep = `e(depvar)') file(model3) long replace
file model3.tex saved

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_7

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore   -0.3734   -0.3734   -0.3682   -0.1469   -0.3854

              0       1
Pr(y|x)  0.3904  0.6096

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            47             1
sd_x=       .383848       .476001       .471821       .287893       .500035       .500145       13.6088       .493754

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .490683       .500047       .345143       1.50137       1.84242       .677095       .485716       .456514

            noparty       conserv
   x=             0             5
sd_x=       .496216       2.08467

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_7

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.6115   [ 0.5232,    0.6999]
  Pr(y=0|x):          0.3885   [ 0.3001,    0.4768]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .80000001             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat13 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_7

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.1612   [ 0.0948,    0.2275]
  Pr(y=0|x):          0.8388   [ 0.7725,    0.9052]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             1             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat14 = (t[1,7], t[1,8], t[1,9])

. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_1 > -1

Iteration 0:   log likelihood =  -1210.512  
Iteration 1:   log likelihood =  -1087.342  
Iteration 2:   log likelihood = -1087.2513  
Iteration 3:   log likelihood = -1087.2513  

Logistic regression                             Number of obs     =      1,760
                                                LR chi2(18)       =     246.52
                                                Prob > chi2       =     0.0000
Log likelihood = -1087.2513                     Pseudo R2         =     0.1018

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |     .88626   .1443772     6.14   0.000      .603286    1.169234
  hist_japan |   .4091853   .1270904     3.22   0.001     .1600927    .6582779
hist_foreign |      -.346   .1273566    -2.72   0.007    -.5956144   -.0963856
 nationalism |   1.124299   .1998836     5.62   0.000     .7325348    1.516064
    powerful |   .1985118   .1033138     1.92   0.055    -.0039795    .4010031
    valuable |   .2267371   .1036596     2.19   0.029     .0235681    .4299061
         age |   .0000222    .004159     0.01   0.996    -.0081292    .0081737
        male |   .4907384   .1196482     4.10   0.000     .2562323    .7252445
     college |  -.0633175   .1151203    -0.55   0.582    -.2889493    .1623142
    fulltime |   .0077134    .125985     0.06   0.951    -.2392128    .2546395
    parttime |  -.0886605   .1642636    -0.54   0.589    -.4106111    .2332902
      income |   .0089533   .0405539     0.22   0.825    -.0705309    .0884374
socialstatus |  -.0758894   .0342102    -2.22   0.027    -.1429402   -.0088386
        news |    .177383   .0839845     2.11   0.035     .0127764    .3419896
     defense |   .4838495   .1103254     4.39   0.000     .2676157    .7000833
         ldp |   .1230085   .1507858     0.82   0.415    -.1725261    .4185432
     noparty |  -.0293616   .1290128    -0.23   0.820    -.2822221    .2234988
     conserv |   .0669801   .0280192     2.39   0.017     .0120634    .1218968
       _cons |  -2.347079   .3862789    -6.08   0.000    -3.104171   -1.589986
------------------------------------------------------------------------------

. estimates store o1

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. *outtex, detail level below title(Dep = `e(depvar)') file(model3) long replace
. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_1

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore    0.2113    0.2113    0.2177    0.0825    0.2212

              0       1
Pr(y|x)  0.5198  0.4802

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            48             1
sd_x=       .373629       .472005       .476566       .283624       .500037       .500126       13.5655       .496431

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .489568       .499832       .345665        1.5072       1.80486       .676742       .481662       .453522

            noparty       conserv
   x=             0             5
sd_x=       .497281        2.0777

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_1

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.4802   [ 0.3905,    0.5698]
  Pr(y=0|x):          0.5198   [ 0.4302,    0.6095]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .80000001             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat1 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_1

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.7560   [ 0.6740,    0.8380]
  Pr(y=0|x):          0.2440   [ 0.1620,    0.3260]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat2 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_6 > -1

Iteration 0:   log likelihood = -650.57089  
Iteration 1:   log likelihood = -623.07503  
Iteration 2:   log likelihood = -621.38804  
Iteration 3:   log likelihood = -621.38332  
Iteration 4:   log likelihood = -621.38332  

Logistic regression                             Number of obs     =      1,963
                                                LR chi2(18)       =      58.38
                                                Prob > chi2       =     0.0000
Log likelihood = -621.38332                     Pseudo R2         =     0.0449

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -.0377941   .2140182    -0.18   0.860    -.4572621     .381674
  hist_japan |   .3751649   .1924792     1.95   0.051    -.0020875    .7524172
hist_foreign |   .0838144    .181082     0.46   0.643    -.2710999    .4387286
 nationalism |    .774536   .2640608     2.93   0.003     .2569864    1.292086
    powerful |   .3947968    .152694     2.59   0.010     .0955221    .6940716
    valuable |   .0214299   .1521192     0.14   0.888    -.2767183    .3195781
         age |    .023774   .0060674     3.92   0.000      .011882    .0356659
        male |  -.1131954   .1718221    -0.66   0.510    -.4499605    .2235696
     college |   .1176226   .1703112     0.69   0.490    -.2161812    .4514265
    fulltime |  -.0181975   .1771134    -0.10   0.918    -.3653334    .3289385
    parttime |   .4711523    .272982     1.73   0.084    -.0638826    1.006187
      income |    .061416   .0580128     1.06   0.290     -.052287     .175119
socialstatus |   -.083145   .0486894    -1.71   0.088    -.1785745    .0122846
        news |   .2488939   .1165246     2.14   0.033     .0205098     .477278
     defense |  -.1909015   .1614898    -1.18   0.237    -.5074157    .1256126
         ldp |    .365072   .2177836     1.68   0.094    -.0617761      .79192
     noparty |    .334381   .1841206     1.82   0.069    -.0264887    .6952507
     conserv |  -.0401436   .0409028    -0.98   0.326    -.1203116    .0400243
       _cons |  -.2933771    .543246    -0.54   0.589     -1.35812    .7713655
------------------------------------------------------------------------------

. estimates store o6

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_6

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore   -0.0034   -0.0034   -0.0034   -0.0013   -0.0034

              0       1
Pr(y|x)  0.0995  0.9005

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            48             1
sd_x=       .373152       .473237       .475616       .280878        .49987       .500039       13.5333       .496531

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .489474       .499384       .347171       1.51321       1.82093       .675632       .482331       .452145

            noparty       conserv
   x=             0             5
sd_x=       .497654       2.04588

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_6

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.8857   [ 0.8350,    0.9364]
  Pr(y=0|x):          0.1143   [ 0.0636,    0.1650]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .60000002             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat11 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_6

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.8686   [ 0.7958,    0.9413]
  Pr(y=0|x):          0.1314   [ 0.0587,    0.2042]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat12 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_2 > -1

Iteration 0:   log likelihood = -1107.8353  
Iteration 1:   log likelihood = -1002.8035  
Iteration 2:   log likelihood = -1000.7669  
Iteration 3:   log likelihood = -1000.7635  
Iteration 4:   log likelihood = -1000.7635  

Logistic regression                             Number of obs     =      1,809
                                                LR chi2(18)       =     214.14
                                                Prob > chi2       =     0.0000
Log likelihood = -1000.7635                     Pseudo R2         =     0.0966

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .8934893   .1425682     6.27   0.000     .6140607    1.172918
  hist_japan |   .2110072   .1340091     1.57   0.115    -.0516459    .4736603
hist_foreign |  -.2030815   .1366999    -1.49   0.137    -.4710084    .0648454
 nationalism |   .8730428   .2138017     4.08   0.000     .4539992    1.292086
    powerful |   .0624586    .109667     0.57   0.569    -.1524849     .277402
    valuable |   .0694339   .1102894     0.63   0.529    -.1467294    .2855973
         age |   -.026187   .0044035    -5.95   0.000    -.0348178   -.0175562
        male |   .6324846   .1289875     4.90   0.000     .3796737    .8852956
     college |  -.1027185   .1230422    -0.83   0.404    -.3438768    .1384399
    fulltime |  -.0950029   .1321149    -0.72   0.472    -.3539433    .1639375
    parttime |  -.1181098   .1815807    -0.65   0.515    -.4740015     .237782
      income |   .0294776   .0426394     0.69   0.489    -.0540942    .1130493
socialstatus |  -.1244336   .0355081    -3.50   0.000    -.1940281   -.0548391
        news |   .0706306   .0873691     0.81   0.419    -.1006097    .2418708
     defense |   .4419606   .1152021     3.84   0.000     .2161687    .6677525
         ldp |   .2699286     .15751     1.71   0.087    -.0387853    .5786424
     noparty |   .0387516   .1403189     0.28   0.782    -.2362685    .3137716
     conserv |    .071983   .0294894     2.44   0.015     .0141849    .1297811
       _cons |  -1.036062   .4000875    -2.59   0.010    -1.820219   -.2519047
------------------------------------------------------------------------------

. estimates store o2

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_2

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore    0.2095    0.2095    0.1826    0.0665    0.1840

              0       1
Pr(y|x)  0.7099  0.2901

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            48             1
sd_x=       .361796        .47173       .477075       .283194       .500045        .50001       13.6454         .4959

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=        .48856       .499586       .340505       1.52232       1.82953       .677637       .482438       .451916

            noparty       conserv
   x=             0             5
sd_x=       .497684       2.05384

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_2

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.2555   [ 0.1846,    0.3264]
  Pr(y=0|x):          0.7445   [ 0.6736,    0.8154]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .60000002             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat3 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_2

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.5925   [ 0.4872,    0.6978]
  Pr(y=0|x):          0.4075   [ 0.3022,    0.5128]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             1             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat4 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_3 > -1

Iteration 0:   log likelihood = -1284.0812  
Iteration 1:   log likelihood =  -1136.372  
Iteration 2:   log likelihood = -1136.2206  
Iteration 3:   log likelihood = -1136.2206  

Logistic regression                             Number of obs     =      1,859
                                                LR chi2(18)       =     295.72
                                                Prob > chi2       =     0.0000
Log likelihood = -1136.2206                     Pseudo R2         =     0.1151

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7463685   .1420332     5.25   0.000     .4679884    1.024749
  hist_japan |   .0948336   .1252502     0.76   0.449    -.1506524    .3403195
hist_foreign |   -.399764   .1248725    -3.20   0.001    -.6445097   -.1550183
 nationalism |    1.14031   .1928379     5.91   0.000     .7623548    1.518266
    powerful |   .2432756   .1014157     2.40   0.016     .0445045    .4420467
    valuable |   .0533348   .1014789     0.53   0.599    -.1455601    .2522297
         age |  -.0271624   .0040992    -6.63   0.000    -.0351967   -.0191281
        male |   .3393746   .1171476     2.90   0.004     .1097696    .5689796
     college |  -.1069965   .1131013    -0.95   0.344     -.328671     .114678
    fulltime |   .0858814   .1229334     0.70   0.485    -.1550637    .3268264
    parttime |   .0934539    .160735     0.58   0.561    -.2215808    .4084887
      income |   .0430759   .0392849     1.10   0.273     -.033921    .1200729
socialstatus |  -.0835572   .0330056    -2.53   0.011    -.1482469   -.0188675
        news |   .1275733    .082388     1.55   0.122    -.0339042    .2890507
     defense |    .570798   .1076704     5.30   0.000     .3597679     .781828
         ldp |   .5626197   .1460363     3.85   0.000     .2763939    .8488455
     noparty |   .0364243   .1267326     0.29   0.774    -.2119672    .2848157
     conserv |    .075406    .027266     2.77   0.006     .0219656    .1288464
       _cons |  -.8576331   .3784838    -2.27   0.023    -1.599448   -.1158185
------------------------------------------------------------------------------

. estimates store o3

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_3

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore    0.1838    0.1838    0.1817    0.0682    0.1837

              0       1
Pr(y|x)  0.5619  0.4381

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            48             1
sd_x=       .371894       .472908       .475609       .284757        .49979       .500122       13.5903       .495104

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .490074       .499683        .34412       1.51624       1.82294       .665692        .48178       .455337

            noparty       conserv
   x=             0             5
sd_x=       .496523       2.07136

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_3

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.3829   [ 0.3012,    0.4647]
  Pr(y=0|x):          0.6171   [ 0.5353,    0.6988]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .60000002             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat5 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_3

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.7691   [ 0.6946,    0.8436]
  Pr(y=0|x):          0.2309   [ 0.1564,    0.3054]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             1             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat6 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv  if q100_4 > -1

Iteration 0:   log likelihood = -1106.2905  
Iteration 1:   log likelihood = -965.91351  
Iteration 2:   log likelihood = -960.22702  
Iteration 3:   log likelihood = -960.21264  
Iteration 4:   log likelihood = -960.21264  

Logistic regression                             Number of obs     =      1,920
                                                LR chi2(18)       =     292.16
                                                Prob > chi2       =     0.0000
Log likelihood = -960.21264                     Pseudo R2         =     0.1320

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7720448   .1428239     5.41   0.000     .4921151    1.051975
  hist_japan |  -.1147736   .1393301    -0.82   0.410    -.3878555    .1583083
hist_foreign |  -.2327022   .1398084    -1.66   0.096    -.5067216    .0413173
 nationalism |   1.131643     .22381     5.06   0.000      .692983    1.570302
    powerful |  -.0596338   .1133541    -0.53   0.599    -.2818038    .1625362
    valuable |   .0100505    .113539     0.09   0.929    -.2124818    .2325828
         age |  -.0276164   .0045581    -6.06   0.000      -.03655   -.0186827
        male |   .7677094   .1350281     5.69   0.000     .5030593     1.03236
     college |  -.3704442   .1270317    -2.92   0.004    -.6194218   -.1214667
    fulltime |   .2381495   .1366058     1.74   0.081    -.0295929    .5058919
    parttime |  -.2523263   .1948706    -1.29   0.195    -.6342657    .1296131
      income |  -.0316098   .0445886    -0.71   0.478    -.1190018    .0557822
socialstatus |  -.0532744   .0363581    -1.47   0.143     -.124535    .0179861
        news |     .17338   .0923457     1.88   0.060    -.0076142    .3543743
     defense |   .6427098   .1171836     5.48   0.000     .4130341    .8723855
         ldp |   .2501099   .1579691     1.58   0.113    -.0595039    .5597236
     noparty |  -.1527663   .1457391    -1.05   0.295    -.4384098    .1328772
     conserv |   .0969857   .0300858     3.22   0.001     .0380187    .1559527
       _cons |   -1.82989   .4164087    -4.39   0.000    -2.646036   -1.013744
------------------------------------------------------------------------------

. estimates store o4

. outreg2 using tableA9, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA9.tex
tableA9.xml
dir : seeout

. prchange hardcore, rest(median)

logit: Changes in Probabilities for q100_4

          min->max      0->1     -+1/2    -+sd/2  MargEfct
hardcore    0.1595    0.1595    0.1329    0.0484    0.1330

              0       1
Pr(y|x)  0.7788  0.2212

           hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male
   x=             0             0             0            .8             1             1            48             1
sd_x=        .36417       .472077       .477094       .283079       .499798       .500052       13.6678        .49713

            college      fulltime      parttime        income  socialstatus          news       defense           ldp
   x=             1             0             0             3             5             3             0             0
sd_x=       .490237       .499248       .346645       1.51004        1.8235       .673625       .480996       .450734

            noparty       conserv
   x=             0             5
sd_x=       .497571       2.06411

. estadd prvalue, x(hardcore=0 ) rest(grmedian) replace

logit: Predictions for q100_4

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.1847   [ 0.1261,    0.2432]
  Pr(y=0|x):          0.8153   [ 0.7568,    0.8739]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             0             0             0     .60000002             1             1            48             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             0             0             0             5

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat7 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. estadd prvalue, x(hardcore=1 ) rest(grmedian) replace

logit: Predictions for q100_4

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.5673   [ 0.4541,    0.6804]
  Pr(y=0|x):          0.4327   [ 0.3196,    0.5459]

        hardcore    hist_japan  hist_foreign   nationalism      powerful      valuable           age          male       college
x=             1             0             0     .80000001             1             0            47             1             1

        fulltime      parttime        income  socialstatus          news       defense           ldp       noparty       conserv
x=             0             0             3             5             3             1             0             0             6

added matrices:
    e(_estadd_prvalue) :  1 x 12
  e(_estadd_prvalue_x) :  1 x 18

. matrix t = e(_estadd_prvalue)

. matrix mat8 = (t[1,7], t[1,8], t[1,9])

. matrix drop t

. 
. 
. **Table Table 9 in Appendix I (abridged Table 1 in main text)
. seeout, lab
Hit Enter to continue. 

. 
. **Figure 4
. 
. matrix t1 = mat9\mat13\mat1\mat11\mat3\mat5\mat7

. matrix rownames t1 = c e a d b f g

. 
. matrix t2 = mat10\mat14\mat2\mat12\mat4\mat6\mat8

. matrix rownames t2 = c e a d b f g

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Compromise Possible) ) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Territory Indivisible) ),  ///
>  coeflabel(a = "Publicity" ///
>            b = `" "Economic" "Sanction" "' ///
>                    f = `" "Limited" "Military Action" "' ///
>                    g = `" "Full" "Military Action" "' ///
>            c = `" "Bilateral" "Negotiation" "' ///
>            d = `" "IO" "Arbitration" "' ///
>            e = `" "Shelving" "the Dispute" "') ///
> mlabel format(%9.2g) mlabposition(1) vertical ///
> ytitle(Predicted Probability of Support) legend(pos(6) col(2)) scheme(plotplain)

. graph export figure4.pdf, replace
(file figure4.pdf written in PDF format)

. 
. //Figure 5: treatment effect by real country
. 
. mean a04 if a04 < 2 & hist_japan == 1 & a14x_china == 1

Mean estimation                   Number of obs   =        226

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .8362832   .0246679      .7876735    .8848928
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1 & a14x_china == 1

Mean estimation                   Number of obs   =        209

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .6411483   .0332587       .575581    .7067157
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1 & a14x_china == 1

Mean estimation                   Number of obs   =        206

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7184466   .0314124      .6565139    .7803793
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. matrix t1 = mat1\mat2\mat3

. matrix rownames t1 = a b c

. 
. mean a04 if a04 < 2 & hist_japan == 1 & a14x_russia == 1

Mean estimation                   Number of obs   =        147

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7482993   .0359173      .6773144    .8192843
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1 & a14x_russia == 1

Mean estimation                   Number of obs   =        116

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .5948276    .045779      .5041481     .685507
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1 & a14x_russia == 1

Mean estimation                   Number of obs   =        128

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7109375   .0402263       .631337     .790538
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. matrix t2 = mat1\mat2\mat3

. matrix rownames t2 = a b c

. 
. mean a04 if a04 < 2 & hist_japan == 1 & a14x_southkorea == 1

Mean estimation                   Number of obs   =        145

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .8482759   .0298961       .789184    .9073678
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1 & a14x_southkorea == 1

Mean estimation                   Number of obs   =        123

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .6178862   .0439917      .5308002    .7049721
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1 & a14x_southkorea == 1

Mean estimation                   Number of obs   =        118

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7966102    .037213      .7229118    .8703086
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. matrix t3 = mat1\mat2\mat3

. matrix rownames t3 = a b c

. 
. mean a04 if a04 < 2 & hist_japan == 1 & a11 == 1

Mean estimation                   Number of obs   =        263

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .7528517   .0266491       .700378    .8053254
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1 & a11 == 1

Mean estimation                   Number of obs   =        321

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .5856698   .0275375      .5314923    .6398472
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1 & a11 == 1

Mean estimation                   Number of obs   =        279

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .6594982   .0284213      .6035499    .7154465
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. matrix t4 = mat1\mat2\mat3

. matrix rownames t4 = a b c

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") ), bylabel(China) || ///
>                 (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") ), bylabel(Russia) || ///
>                 (matrix(t3[.,1]), ci("t3[.,2] t3[.,3]") ), bylabel(South Korea) || ///
>                 (matrix(t4[.,1]), ci("t4[.,2] t4[.,3]") ), bylabel(No Real Country in Mind)  ///
>                  coeflabel(a = `" "Historically owned" "by Japan" "' ///
>                 b = `" "Historically owned" "by foreign" "' ///
>         c = `" "Historically owned" "by neither" "') ///
>                 mlabel format(%9.2g) mlabposition(1) legend(col(1)) ///
>                 ytitle(Proportion of Accepting the Indivisible Outcome, size(small)) ///
>                 vertical scheme(plotplain)

. graph export figure5.pdf, replace
(file figure5.pdf written in PDF format)

. 
. 
. //What countries do you have in mind?
. 
. **no real country in mind
. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_1 > -1 & a11==1

Iteration 0:   log likelihood = -514.85393  
Iteration 1:   log likelihood = -467.95546  
Iteration 2:   log likelihood = -467.72993  
Iteration 3:   log likelihood =  -467.7299  

Logistic regression                             Number of obs     =        765
                                                LR chi2(18)       =      94.25
                                                Prob > chi2       =     0.0000
Log likelihood =  -467.7299                     Pseudo R2         =     0.0915

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7431514   .2274891     3.27   0.001     .2972808    1.189022
  hist_japan |   .2917204   .2007398     1.45   0.146    -.1017224    .6851631
hist_foreign |  -.0738895   .1908638    -0.39   0.699    -.4479756    .3001966
 nationalism |   1.076723   .3006219     3.58   0.000     .4875151    1.665931
    powerful |   .1227173   .1585718     0.77   0.439    -.1880777    .4335123
    valuable |    .126545   .1593587     0.79   0.427    -.1857924    .4388824
         age |   .0066864   .0063486     1.05   0.292    -.0057567    .0191295
        male |   .5903363   .1840083     3.21   0.001     .2296867    .9509859
     college |  -.0893317   .1800138    -0.50   0.620    -.4421522    .2634889
    fulltime |   .2154144   .1888772     1.14   0.254    -.1547781     .585607
    parttime |    .019809   .2601169     0.08   0.939    -.4900109    .5296288
      income |  -.0424669   .0624227    -0.68   0.496    -.1648132    .0798794
socialstatus |  -.0184109   .0504938    -0.36   0.715     -.117377    .0805552
        news |   .1903719   .1261386     1.51   0.131    -.0568552     .437599
     defense |   .5696698   .1678738     3.39   0.001     .2406433    .8986964
         ldp |   .0583306   .2288869     0.25   0.799    -.3902795    .5069406
     noparty |   .0650799   .2017561     0.32   0.747    -.3303548    .4605147
     conserv |   .0728519   .0407242     1.79   0.074    -.0069661    .1526699
       _cons |  -3.121974   .5942197    -5.25   0.000    -4.286623   -1.957325
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_2 > -1 & a11==1

Iteration 0:   log likelihood = -470.51995  
Iteration 1:   log likelihood = -434.95564  
Iteration 2:   log likelihood = -434.14457  
Iteration 3:   log likelihood = -434.14407  
Iteration 4:   log likelihood = -434.14407  

Logistic regression                             Number of obs     =        803
                                                LR chi2(18)       =      72.75
                                                Prob > chi2       =     0.0000
Log likelihood = -434.14407                     Pseudo R2         =     0.0773

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |    1.07062   .2290886     4.67   0.000     .6216149    1.519625
  hist_japan |   .1091371   .2143933     0.51   0.611    -.3110661    .5293403
hist_foreign |  -.0333443    .201392    -0.17   0.868    -.4280654    .3613769
 nationalism |   .3565645   .3159482     1.13   0.259    -.2626826    .9758117
    powerful |   .0201672   .1685415     0.12   0.905    -.3101681    .3505025
    valuable |   -.248316    .170791    -1.45   0.146    -.5830601    .0864282
         age |   -.014453   .0066068    -2.19   0.029     -.027402    -.001504
        male |   .4808827    .194789     2.47   0.014     .0991033    .8626621
     college |   .0360445   .1925367     0.19   0.851    -.3413205    .4134095
    fulltime |  -.0819048   .1986666    -0.41   0.680    -.4712842    .3074745
    parttime |  -.0213414   .2834741    -0.08   0.940    -.5769404    .5342576
      income |  -.0097463   .0656038    -0.15   0.882    -.1383274    .1188348
socialstatus |  -.0721997    .053205    -1.36   0.175    -.1764795      .03208
        news |   .0960125   .1313344     0.73   0.465    -.1613983    .3534233
     defense |   .2998473   .1764207     1.70   0.089    -.0459309    .6456256
         ldp |   .5174471   .2390005     2.17   0.030     .0490147    .9858795
     noparty |  -.0554323    .220998    -0.25   0.802    -.4885804    .3777159
     conserv |   .0417491   .0429041     0.97   0.331    -.0423414    .1258395
       _cons |  -1.284326   .6050541    -2.12   0.034    -2.470211   -.0984423
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_5 > -1 & a11==1

Iteration 0:   log likelihood = -447.07384  
Iteration 1:   log likelihood = -398.94768  
Iteration 2:   log likelihood =  -396.3645  
Iteration 3:   log likelihood = -396.35831  
Iteration 4:   log likelihood = -396.35831  

Logistic regression                             Number of obs     =        823
                                                LR chi2(18)       =     101.43
                                                Prob > chi2       =     0.0000
Log likelihood = -396.35831                     Pseudo R2         =     0.1134

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.710357   .2371051    -7.21   0.000    -2.175074   -1.245639
  hist_japan |  -.5741318   .2267069    -2.53   0.011    -1.018469   -.1297945
hist_foreign |  -.2146445   .2202332    -0.97   0.330    -.6462937    .2170047
 nationalism |   .2236484   .3225233     0.69   0.488    -.4084856    .8557824
    powerful |   .2712086   .1805239     1.50   0.133    -.0826118     .625029
    valuable |  -.3013656   .1821163    -1.65   0.098    -.6583069    .0555758
         age |   .0142465   .0071604     1.99   0.047     .0002124    .0282806
        male |  -.3043562   .2055142    -1.48   0.139    -.7071567    .0984444
     college |    .143642   .2004589     0.72   0.474    -.2492504    .5365343
    fulltime |  -.0831259   .2141332    -0.39   0.698    -.5028192    .3365674
    parttime |  -.1119843    .289777    -0.39   0.699    -.6799367    .4559681
      income |   .0479212   .0700074     0.68   0.494    -.0892909    .1851332
socialstatus |   .0237092   .0567468     0.42   0.676    -.0875125    .1349308
        news |   .2453264   .1370005     1.79   0.073    -.0231896    .5138424
     defense |  -.2534869   .1880395    -1.35   0.178    -.6220375    .1150637
         ldp |  -.8464359   .2691077    -3.15   0.002    -1.373877   -.3189946
     noparty |  -.4548349   .2450675    -1.86   0.063    -.9351583    .0254886
     conserv |  -.0590579   .0466757    -1.27   0.206    -.1505405    .0324247
       _cons |   1.035037   .6399962     1.62   0.106    -.2193326    2.289406
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_6 > -1 & a11==1

Iteration 0:   log likelihood = -322.13859  
Iteration 1:   log likelihood =  -309.1901  
Iteration 2:   log likelihood = -308.55181  
Iteration 3:   log likelihood = -308.54986  
Iteration 4:   log likelihood = -308.54986  

Logistic regression                             Number of obs     =        868
                                                LR chi2(18)       =      27.18
                                                Prob > chi2       =     0.0757
Log likelihood = -308.54986                     Pseudo R2         =     0.0422

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .4085528   .3511656     1.16   0.245    -.2797192    1.096825
  hist_japan |   .0903771   .2652436     0.34   0.733    -.4294909    .6102451
hist_foreign |   .2442452   .2568726     0.95   0.342    -.2592158    .7477062
 nationalism |   .9015362   .3610297     2.50   0.013      .193931    1.609141
    powerful |   .3324548   .2153427     1.54   0.123    -.0896091    .7545187
    valuable |   .0377457   .2140525     0.18   0.860    -.3817894    .4572809
         age |   .0184396   .0086578     2.13   0.033     .0014707    .0354085
        male |  -.2466199   .2429729    -1.02   0.310    -.7228379    .2295982
     college |  -.0364482   .2510747    -0.15   0.885    -.5285456    .4556493
    fulltime |  -.1963233   .2472696    -0.79   0.427    -.6809629    .2883162
    parttime |   .1998057   .3870023     0.52   0.606    -.5587048    .9583163
      income |   .0610191   .0816265     0.75   0.455    -.0989658     .221004
socialstatus |  -.0462375   .0652815    -0.71   0.479    -.1741868    .0817118
        news |   .0948507   .1584982     0.60   0.550    -.2158001    .4055015
     defense |  -.3214488   .2260721    -1.42   0.155     -.764542    .1216443
         ldp |   .3859478    .312246     1.24   0.216    -.2260432    .9979387
     noparty |   .1832779   .2576455     0.71   0.477    -.3216981    .6882538
     conserv |   .0005224   .0541145     0.01   0.992    -.1055401    .1065848
       _cons |   .2098459   .7741771     0.27   0.786    -1.307513    1.727205
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_7 > -1 & a11==1

Iteration 0:   log likelihood =  -502.0684  
Iteration 1:   log likelihood =  -463.6382  
Iteration 2:   log likelihood = -463.27815  
Iteration 3:   log likelihood = -463.27764  
Iteration 4:   log likelihood = -463.27764  

Logistic regression                             Number of obs     =        738
                                                LR chi2(18)       =      77.58
                                                Prob > chi2       =     0.0000
Log likelihood = -463.27764                     Pseudo R2         =     0.0773

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.253542   .2634175    -4.76   0.000    -1.769831   -.7372531
  hist_japan |  -.7132742   .2008245    -3.55   0.000    -1.106883   -.3196654
hist_foreign |  -.5491729   .1891089    -2.90   0.004    -.9198196   -.1785262
 nationalism |    .052984   .2848591     0.19   0.852    -.5053295    .6112976
    powerful |   .0155337   .1588739     0.10   0.922    -.2958534    .3269208
    valuable |   .0906744   .1604127     0.57   0.572    -.2237286    .4050775
         age |   .0049447   .0062179     0.80   0.426    -.0072422    .0171316
        male |     .04126    .184438     0.22   0.823    -.3202319    .4027519
     college |  -.0712636   .1805422    -0.39   0.693    -.4251198    .2825927
    fulltime |  -.0787379   .1863915    -0.42   0.673    -.4440585    .2865827
    parttime |   .0232116   .2617754     0.09   0.929    -.4898587    .5362819
      income |  -.0311625   .0618859    -0.50   0.615    -.1524567    .0901316
socialstatus |   .0929867   .0507207     1.83   0.067     -.006424    .1923975
        news |   .0847913   .1268694     0.67   0.504    -.1638682    .3334507
     defense |  -.3424305   .1720364    -1.99   0.047    -.6796156   -.0052454
         ldp |  -.6693273   .2324423    -2.88   0.004    -1.124906   -.2137488
     noparty |  -.6988891   .2017068    -3.46   0.001    -1.094227    -.303551
     conserv |  -.0663447   .0411922    -1.61   0.107    -.1470799    .0143905
       _cons |   .3401129   .5956448     0.57   0.568    -.8273294    1.507555
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_3 > -1 & a11==1

Iteration 0:   log likelihood = -559.27435  
Iteration 1:   log likelihood = -498.25998  
Iteration 2:   log likelihood = -498.12165  
Iteration 3:   log likelihood = -498.12164  

Logistic regression                             Number of obs     =        817
                                                LR chi2(18)       =     122.31
                                                Prob > chi2       =     0.0000
Log likelihood = -498.12164                     Pseudo R2         =     0.1093

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .8461182   .2242863     3.77   0.000     .4065252    1.285711
  hist_japan |   .0361113   .1963034     0.18   0.854    -.3486362    .4208588
hist_foreign |  -.4128857   .1832529    -2.25   0.024    -.7720548   -.0537167
 nationalism |   .7926823   .2852637     2.78   0.005     .2335757    1.351789
    powerful |   .3034018   .1541706     1.97   0.049     .0012329    .6055706
    valuable |  -.0964349   .1547661    -0.62   0.533    -.3997709    .2069011
         age |  -.0259396   .0061496    -4.22   0.000    -.0379926   -.0138865
        male |   .4746016   .1775244     2.67   0.008     .1266603     .822543
     college |  -.1883951   .1738052    -1.08   0.278     -.529047    .1522568
    fulltime |   .0886363   .1820215     0.49   0.626    -.2681193     .445392
    parttime |   .3168127   .2509512     1.26   0.207    -.1750425    .8086679
      income |   .0482417   .0596238     0.81   0.418    -.0686187    .1651022
socialstatus |  -.0830415   .0491968    -1.69   0.091    -.1794655    .0133824
        news |   .1063459   .1212444     0.88   0.380    -.1312888    .3439807
     defense |   .6550777   .1630358     4.02   0.000     .3355333    .9746221
         ldp |   .6340237   .2240766     2.83   0.005     .1948416    1.073206
     noparty |   .2585483   .1989615     1.30   0.194    -.1314091    .6485057
     conserv |   .0661008   .0395037     1.67   0.094     -.011325    .1435267
       _cons |  -.8704976   .5638386    -1.54   0.123    -1.975601    .2346058
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv if q100_4 > -1 & a11==1

Iteration 0:   log likelihood = -479.39703  
Iteration 1:   log likelihood = -420.11184  
Iteration 2:   log likelihood = -417.44713  
Iteration 3:   log likelihood = -417.44025  
Iteration 4:   log likelihood = -417.44025  

Logistic regression                             Number of obs     =        853
                                                LR chi2(18)       =     123.91
                                                Prob > chi2       =     0.0000
Log likelihood = -417.44025                     Pseudo R2         =     0.1292

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .6561021   .2290284     2.86   0.004     .2072148     1.10499
  hist_japan |   .0471014   .2186798     0.22   0.829    -.3815031     .475706
hist_foreign |    .000986   .2095336     0.00   0.996    -.4096923    .4116644
 nationalism |   1.107093    .328442     3.37   0.001     .4633583    1.750827
    powerful |   -.113387   .1733835    -0.65   0.513    -.4532125    .2264384
    valuable |  -.2391085   .1741721    -1.37   0.170    -.5804795    .1022624
         age |   -.022577   .0068744    -3.28   0.001    -.0360506   -.0091035
        male |   .9582581   .2071781     4.63   0.000     .5521964     1.36432
     college |  -.4631454    .195729    -2.37   0.018    -.8467672   -.0795236
    fulltime |   .2551302   .2051759     1.24   0.214    -.1470072    .6572676
    parttime |  -.1536796   .2981314    -0.52   0.606    -.7380063    .4306472
      income |  -.0155918   .0670897    -0.23   0.816    -.1470853    .1159016
socialstatus |   .0015782    .053816     0.03   0.977    -.1038992    .1070556
        news |  -.0324645   .1358803    -0.24   0.811     -.298785    .2338561
     defense |   .8380631   .1784131     4.70   0.000     .4883798    1.187746
         ldp |   .3229652    .239752     1.35   0.178    -.1469401    .7928705
     noparty |   .0289008   .2261955     0.13   0.898    -.4144342    .4722358
     conserv |   .1044916   .0439078     2.38   0.017     .0184338    .1905493
       _cons |  -2.055525    .617465    -3.33   0.001    -3.265735   -.8453163
------------------------------------------------------------------------------

. outreg2 using tableA5, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA5.tex
tableA5.xml
dir : seeout

. 
. **Table 5 in Appendix
. seeout, lab
Hit Enter to continue. 

. 
. 
. ** thinking about China only
. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_1 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -223.38456  
Iteration 1:   log likelihood = -201.14526  
Iteration 2:   log likelihood = -201.09334  
Iteration 3:   log likelihood = -201.09333  

Logistic regression                             Number of obs     =        325
                                                LR chi2(16)       =      44.58
                                                Prob > chi2       =     0.0002
Log likelihood = -201.09333                     Pseudo R2         =     0.0998

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .0647743   .3301592     0.20   0.844    -.5823258    .7118744
  hist_japan |   .1946174   .2935263     0.66   0.507    -.3806836    .7699185
hist_foreign |   -.791952   .3180269    -2.49   0.013    -1.415273   -.1686307
 nationalism |   1.552332   .5082701     3.05   0.002     .5561409    2.548523
         age |  -.0157932   .0106455    -1.48   0.138     -.036658    .0050716
        male |   .3145441    .292839     1.07   0.283    -.2594098     .888498
     college |  -.3390001   .2742706    -1.24   0.216    -.8765607    .1985605
    fulltime |  -.0030751   .3219959    -0.01   0.992    -.6341755    .6280254
    parttime |   .0483886   .3810624     0.13   0.899    -.6984799    .7952571
      income |   .1013164   .0997532     1.02   0.310    -.0941962    .2968289
socialstatus |  -.1361619   .0886611    -1.54   0.125    -.3099345    .0376107
        news |   .2309595   .2099111     1.10   0.271    -.1804586    .6423777
     defense |   .3992981   .2599901     1.54   0.125    -.1102731    .9088693
         ldp |   .4644815   .3662849     1.27   0.205    -.2534236    1.182387
     noparty |   .1410163   .2912189     0.48   0.628    -.4297622    .7117948
     conserv |   .0505903   .0686899     0.74   0.461    -.0840395    .1852201
       _cons |  -1.300385   .9732954    -1.34   0.182    -3.208009    .6072388
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a1

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_2 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -190.60127  
Iteration 1:   log likelihood = -170.96189  
Iteration 2:   log likelihood = -170.32992  
Iteration 3:   log likelihood = -170.32827  
Iteration 4:   log likelihood = -170.32827  

Logistic regression                             Number of obs     =        316
                                                LR chi2(16)       =      40.55
                                                Prob > chi2       =     0.0006
Log likelihood = -170.32827                     Pseudo R2         =     0.1064

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .1208319   .3659907     0.33   0.741    -.5964967    .8381606
  hist_japan |    .305415   .3209301     0.95   0.341    -.3235965    .9344265
hist_foreign |  -.5444066   .3594093    -1.51   0.130    -1.248836    .1600226
 nationalism |   1.090314   .5850304     1.86   0.062    -.0563244    2.236953
         age |  -.0316425   .0115632    -2.74   0.006     -.054306   -.0089789
        male |   .1858865   .3241087     0.57   0.566    -.4493548    .8211278
     college |  -.4105191   .3134912    -1.31   0.190    -1.024951    .2039123
    fulltime |   .1555431   .3520099     0.44   0.659    -.5343837    .8454698
    parttime |   .1103447   .4323621     0.26   0.799    -.7370694    .9577588
      income |   .1483568   .1100763     1.35   0.178    -.0673888    .3641023
socialstatus |  -.1742055   .0960394    -1.81   0.070    -.3624393    .0140284
        news |   .0150656   .2326158     0.06   0.948     -.440853    .4709842
     defense |   .5259258   .2876671     1.83   0.068    -.0378914    1.089743
         ldp |   .2336655   .3838936     0.61   0.543    -.5187521    .9860832
     noparty |  -.0002861   .3332919    -0.00   0.999    -.6535263    .6529541
     conserv |   .1197222   .0755406     1.58   0.113    -.0283347     .267779
       _cons |  -.6205653   1.083865    -0.57   0.567    -2.744901     1.50377
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a2

. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_5 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -175.37998  
Iteration 1:   log likelihood = -150.02667  
Iteration 2:   log likelihood = -148.53532  
Iteration 3:   log likelihood = -148.52965  
Iteration 4:   log likelihood = -148.52965  

Logistic regression                             Number of obs     =        329
                                                LR chi2(16)       =      53.70
                                                Prob > chi2       =     0.0000
Log likelihood = -148.52965                     Pseudo R2         =     0.1531

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.656228   .3741471    -4.43   0.000    -2.389543   -.9229136
  hist_japan |  -.8634204   .3714719    -2.32   0.020    -1.591492   -.1353489
hist_foreign |  -.1978266   .3947173    -0.50   0.616    -.9714583    .5758051
 nationalism |   .3558346   .5877862     0.61   0.545    -.7962052    1.507874
         age |   .0280843   .0126443     2.22   0.026      .003302    .0528666
        male |  -.5141658   .3632397    -1.42   0.157    -1.226103    .1977709
     college |   .5806451   .3347753     1.73   0.083    -.0755025    1.236793
    fulltime |   .0302754   .3934791     0.08   0.939    -.7409293    .8014802
    parttime |   .3488306   .5098579     0.68   0.494    -.6504724    1.348134
      income |   .1424827   .1189377     1.20   0.231     -.090631    .3755964
socialstatus |   .0540467   .1052272     0.51   0.608    -.1521947    .2602882
        news |   .0278173   .2497437     0.11   0.911    -.4616713    .5173059
     defense |   -.281403   .3223561    -0.87   0.383    -.9132092    .3504033
         ldp |   .1856167   .4237576     0.44   0.661    -.6449329    1.016166
     noparty |  -.0867068   .3537517    -0.25   0.806    -.7800475    .6066339
     conserv |  -.0457993   .0825251    -0.55   0.579    -.2075455     .115947
       _cons |  -.1552843   1.140133    -0.14   0.892    -2.389903    2.079335
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a3

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_6 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -92.372588  
Iteration 1:   log likelihood = -86.507965  
Iteration 2:   log likelihood = -85.622687  
Iteration 3:   log likelihood = -85.620756  
Iteration 4:   log likelihood = -85.620756  

Logistic regression                             Number of obs     =        347
                                                LR chi2(16)       =      13.50
                                                Prob > chi2       =     0.6356
Log likelihood = -85.620756                     Pseudo R2         =     0.0731

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -.2893195   .5261684    -0.55   0.582    -1.320591    .7419516
  hist_japan |   .2306104   .5433127     0.42   0.671     -.834263    1.295484
hist_foreign |  -.1564756   .5268176    -0.30   0.766    -1.189019     .876068
 nationalism |   .1496314   .8439658     0.18   0.859    -1.504511    1.803774
         age |   .0460505   .0167335     2.75   0.006     .0132534    .0788476
        male |   .1367922   .5018676     0.27   0.785    -.8468503    1.120435
     college |   .3763653   .4670387     0.81   0.420    -.5390138    1.291744
    fulltime |   .3970983    .534065     0.74   0.457    -.6496499    1.443846
    parttime |   1.067077   .8347033     1.28   0.201    -.5689116    2.703065
      income |  -.0841485   .1711353    -0.49   0.623    -.4195674    .2512705
socialstatus |   .0377791   .1472509     0.26   0.798    -.2508274    .3263855
        news |  -.2124765   .3591954    -0.59   0.554    -.9164866    .4915336
     defense |   .0376639   .4604252     0.08   0.935    -.8647529    .9400807
         ldp |   .2429554   .5752111     0.42   0.673    -.8844376    1.370348
     noparty |   .6485275   .5159886     1.26   0.209    -.3627915    1.659847
     conserv |   -.012501   .1134313    -0.11   0.912    -.2348222    .2098202
       _cons |   .1419964   1.706913     0.08   0.934    -3.203491    3.487484
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a4

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_7 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -197.04416  
Iteration 1:   log likelihood = -170.65651  
Iteration 2:   log likelihood = -169.75557  
Iteration 3:   log likelihood = -169.75241  
Iteration 4:   log likelihood = -169.75241  

Logistic regression                             Number of obs     =        301
                                                LR chi2(16)       =      54.58
                                                Prob > chi2       =     0.0000
Log likelihood = -169.75241                     Pseudo R2         =     0.1385

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.642203   .4807914    -3.42   0.001    -2.584537   -.6998692
  hist_japan |   -.832675   .3388344    -2.46   0.014    -1.496778   -.1685717
hist_foreign |    .085085     .33159     0.26   0.797    -.5648194    .7349895
 nationalism |   -.487663   .5279618    -0.92   0.356    -1.522449    .5471231
         age |   .0200563   .0116191     1.73   0.084    -.0027168    .0428293
        male |  -.5481282   .3178106    -1.72   0.085    -1.171025    .0747691
     college |   .5131614   .3019487     1.70   0.089    -.0786471     1.10497
    fulltime |   .6634335   .3543731     1.87   0.061     -.031125    1.357992
    parttime |    .553944   .4271098     1.30   0.195    -.2831759    1.391064
      income |   .0039057   .1112315     0.04   0.972     -.214104    .2219154
socialstatus |  -.0375424   .0961149    -0.39   0.696    -.2259242    .1508394
        news |    .355004   .2304028     1.54   0.123    -.0965771    .8065851
     defense |  -.6897167   .2962339    -2.33   0.020    -1.270325   -.1091089
         ldp |  -.0665321   .3991256    -0.17   0.868    -.8488039    .7157396
     noparty |  -.3475476   .3138185    -1.11   0.268    -.9626205    .2675253
     conserv |  -.0711319      .0711    -1.00   0.317    -.2104854    .0682215
       _cons |  -1.366705    1.10706    -1.23   0.217    -3.536503    .8030935
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a5

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_3 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -230.02847  
Iteration 1:   log likelihood = -204.72654  
Iteration 2:   log likelihood = -204.70391  
Iteration 3:   log likelihood = -204.70391  

Logistic regression                             Number of obs     =        332
                                                LR chi2(16)       =      50.65
                                                Prob > chi2       =     0.0000
Log likelihood = -204.70391                     Pseudo R2         =     0.1101

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .4895757   .3493579     1.40   0.161    -.1951533    1.174305
  hist_japan |   .4157826    .295959     1.40   0.160    -.1642864    .9958517
hist_foreign |  -.3653861   .3044742    -1.20   0.230    -.9621446    .2313724
 nationalism |   .7453731   .4829357     1.54   0.123    -.2011635     1.69191
         age |  -.0231523   .0104212    -2.22   0.026    -.0435776   -.0027271
        male |   .1968553   .2812265     0.70   0.484    -.3543384    .7480491
     college |   -.419824   .2740557    -1.53   0.126    -.9569632    .1173152
    fulltime |   .2005787   .3184961     0.63   0.529    -.4236621    .8248195
    parttime |  -.0678976   .3857592    -0.18   0.860    -.8239718    .6881766
      income |   .0864317   .0980842     0.88   0.378    -.1058097    .2786731
socialstatus |    -.03562   .0849425    -0.42   0.675    -.2021043    .1308643
        news |  -.0638145   .2078337    -0.31   0.759    -.4711611    .3435321
     defense |   .0772443   .2609352     0.30   0.767    -.4341794    .5886679
         ldp |   .6614342   .3485712     1.90   0.058    -.0217529    1.344621
     noparty |  -.0915381   .2820093    -0.32   0.745    -.6442661    .4611899
     conserv |   .1607272   .0666659     2.41   0.016     .0300645    .2913899
       _cons |  -.3675009   1.004711    -0.37   0.715    -2.336698    1.601696
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a6

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_4 > -1 & a14x_china==1 & dup<2

Iteration 0:   log likelihood = -185.76997  
Iteration 1:   log likelihood = -162.29864  
Iteration 2:   log likelihood = -160.95367  
Iteration 3:   log likelihood = -160.94935  
Iteration 4:   log likelihood = -160.94935  

Logistic regression                             Number of obs     =        341
                                                LR chi2(16)       =      49.64
                                                Prob > chi2       =     0.0000
Log likelihood = -160.94935                     Pseudo R2         =     0.1336

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .6600485   .3460759     1.91   0.056    -.0182477    1.338345
  hist_japan |   .0075765   .3405885     0.02   0.982    -.6599647    .6751176
hist_foreign |  -.4106823   .3652413    -1.12   0.261    -1.126542    .3051776
 nationalism |   .1268436   .5667657     0.22   0.823    -.9839967    1.237684
         age |  -.0246284   .0119261    -2.07   0.039    -.0480032   -.0012536
        male |   .2874129   .3434395     0.84   0.403    -.3857161     .960542
     college |  -.3644867   .3227546    -1.13   0.259    -.9970742    .2681007
    fulltime |   .5173986   .3815787     1.36   0.175    -.2304819    1.265279
    parttime |  -.2990011   .5233369    -0.57   0.568    -1.324722    .7267203
      income |     .06288   .1170362     0.54   0.591    -.1665067    .2922667
socialstatus |  -.1048278   .0987351    -1.06   0.288    -.2983451    .0886895
        news |    .547791   .2504861     2.19   0.029     .0568472    1.038735
     defense |     .18005   .2996196     0.60   0.548    -.4071935    .7672936
         ldp |   .4580145   .3857065     1.19   0.235    -.2979564    1.213985
     noparty |   -.576338   .3570148    -1.61   0.106    -1.276074    .1233982
     conserv |   .1191604    .077909     1.53   0.126    -.0335385    .2718592
       _cons |  -2.418299   1.144663    -2.11   0.035    -4.661798    -.174801
------------------------------------------------------------------------------

. outreg2 using tableA6, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA6.tex
tableA6.xml
dir : seeout

. estimates store a7

. 
. **Table 6 in Appendix
. seeout, lab
Hit Enter to continue. 

. 
. 
. **Thinking about Russia only
. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_1 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -100.32101  
Iteration 1:   log likelihood = -79.085943  
Iteration 2:   log likelihood = -78.951425  
Iteration 3:   log likelihood = -78.951218  
Iteration 4:   log likelihood = -78.951218  

Logistic regression                             Number of obs     =        146
                                                LR chi2(16)       =      42.74
                                                Prob > chi2       =     0.0003
Log likelihood = -78.951218                     Pseudo R2         =     0.2130

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |    2.88627   .7899708     3.65   0.000     1.337956    4.434585
  hist_japan |  -.0271889   .5204329    -0.05   0.958    -1.047219    .9928408
hist_foreign |  -.0023481   .5059595    -0.00   0.996    -.9940106    .9893143
 nationalism |   2.317816   .8497061     2.73   0.006     .6524228    3.983209
         age |   .0027931   .0172201     0.16   0.871    -.0309577    .0365439
        male |   1.080244   .4745172     2.28   0.023     .1502075    2.010281
     college |   .6681699   .4627035     1.44   0.149    -.2387122    1.575052
    fulltime |  -.6570194   .5268834    -1.25   0.212    -1.689692     .375653
    parttime |  -.6487017   .6965232    -0.93   0.352    -2.013862    .7164587
      income |    .307777   .1666916     1.85   0.065    -.0189325    .6344865
socialstatus |  -.1137263   .1422816    -0.80   0.424    -.3925931    .1651405
        news |   .4228548   .3540146     1.19   0.232    -.2710009    1.116711
     defense |    .822005   .4546365     1.81   0.071    -.0690662    1.713076
         ldp |  -.6357165   .6152376    -1.03   0.301     -1.84156    .5701271
     noparty |   .1350935   .5196195     0.26   0.795     -.883342    1.153529
     conserv |   .2252553   .1273137     1.77   0.077     -.024275    .4747856
       _cons |  -5.831188   1.741301    -3.35   0.001    -9.244075   -2.418301
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b1

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_2 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -77.609137  
Iteration 1:   log likelihood = -61.814229  
Iteration 2:   log likelihood = -60.246667  
Iteration 3:   log likelihood = -60.225583  
Iteration 4:   log likelihood = -60.225568  
Iteration 5:   log likelihood = -60.225568  

Logistic regression                             Number of obs     =        140
                                                LR chi2(16)       =      34.77
                                                Prob > chi2       =     0.0043
Log likelihood = -60.225568                     Pseudo R2         =     0.2240

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   1.965332   .8809647     2.23   0.026     .2386733    3.691992
  hist_japan |   .1672793   .5668652     0.30   0.768     -.943756    1.278315
hist_foreign |  -.8533326   .6033299    -1.41   0.157    -2.035838    .3291723
 nationalism |    1.01702   1.011281     1.01   0.315    -.9650541    2.999095
         age |  -.0417752   .0214242    -1.95   0.051    -.0837659    .0002155
        male |   1.799476   .6227022     2.89   0.004     .5790024     3.01995
     college |    1.32079   .5614996     2.35   0.019     .2202711    2.421309
    fulltime |  -.2761127   .6198557    -0.45   0.656    -1.491008    .9387822
    parttime |  -.2717752   .9113218    -0.30   0.766    -2.057933    1.514383
      income |  -.0279703   .2003496    -0.14   0.889    -.4206482    .3647077
socialstatus |  -.2947734   .1681836    -1.75   0.080    -.6244072    .0348604
        news |   .0500185   .3717797     0.13   0.893    -.6786563    .7786933
     defense |   .5408293   .5424351     1.00   0.319    -.5223239    1.603983
         ldp |  -.1607802   .7369766    -0.22   0.827    -1.605228    1.283667
     noparty |   .0835635   .6344208     0.13   0.895    -1.159878    1.327005
     conserv |    .275987   .1517622     1.82   0.069    -.0214614    .5734353
       _cons |  -2.015999    1.80681    -1.12   0.265    -5.557282    1.525285
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b2

. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_5 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -61.724783  
Iteration 1:   log likelihood = -53.365764  
Iteration 2:   log likelihood = -51.387126  
Iteration 3:   log likelihood = -51.321748  
Iteration 4:   log likelihood = -51.321599  
Iteration 5:   log likelihood = -51.321599  

Logistic regression                             Number of obs     =        145
                                                LR chi2(16)       =      20.81
                                                Prob > chi2       =     0.1861
Log likelihood = -51.321599                     Pseudo R2         =     0.1685

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -2.131346   .7933521    -2.69   0.007    -3.686287   -.5764042
  hist_japan |   .0172215   .6975585     0.02   0.980    -1.349968    1.384411
hist_foreign |   .1627389   .7025435     0.23   0.817    -1.214221    1.539699
 nationalism |   1.789692   1.098204     1.63   0.103    -.3627492    3.942133
         age |   .0213021   .0227562     0.94   0.349    -.0232992    .0659033
        male |   .6596935   .6076819     1.09   0.278    -.5313412    1.850728
     college |   .4881143   .6256645     0.78   0.435    -.7381656    1.714394
    fulltime |  -.2888238   .6953377    -0.42   0.678    -1.651661    1.074013
    parttime |  -.7375748    .885962    -0.83   0.405    -2.474028    .9988787
      income |  -.2789511   .2250011    -1.24   0.215    -.7199452     .162043
socialstatus |  -.0487473   .1989992    -0.24   0.806    -.4387786     .341284
        news |   -.277429   .4548736    -0.61   0.542    -1.168965    .6141068
     defense |   .2609645   .6139917     0.43   0.671    -.9424372    1.464366
         ldp |  -.5734209   .8752776    -0.66   0.512    -2.288933    1.142092
     noparty |   .4213419   .7686829     0.55   0.584    -1.085249    1.927933
     conserv |   -.156732   .1882423    -0.83   0.405    -.5256801     .212216
       _cons |   2.274426   2.061514     1.10   0.270    -1.766066    6.314919
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b3

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_6 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -49.976263  
Iteration 1:   log likelihood = -36.309793  
Iteration 2:   log likelihood = -28.748694  
Iteration 3:   log likelihood = -27.563734  
Iteration 4:   log likelihood = -27.515288  
Iteration 5:   log likelihood = -27.515202  
Iteration 6:   log likelihood = -27.515202  

Logistic regression                             Number of obs     =        162
                                                LR chi2(16)       =      44.92
                                                Prob > chi2       =     0.0001
Log likelihood = -27.515202                     Pseudo R2         =     0.4494

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   1.601071   1.609607     0.99   0.320    -1.553701    4.755843
  hist_japan |    2.94623   1.361388     2.16   0.030     .2779592    5.614501
hist_foreign |   .5180241   .9500038     0.55   0.586    -1.343949    2.379997
 nationalism |   1.519394   1.485162     1.02   0.306     -1.39147    4.430258
         age |    .120592   .0477952     2.52   0.012     .0269151    .2142689
        male |   .2114004    .955433     0.22   0.825    -1.661214    2.084015
     college |    .635337    1.00727     0.63   0.528    -1.338876     2.60955
    fulltime |   -2.18024   1.095298    -1.99   0.047    -4.326986   -.0334949
    parttime |  -1.496211   1.449861    -1.03   0.302    -4.337887    1.345464
      income |  -.0263497   .3484862    -0.08   0.940      -.70937    .6566706
socialstatus |   .3199288   .2871181     1.11   0.265    -.2428124      .88267
        news |   1.333714   .5975181     2.23   0.026     .1625999    2.504828
     defense |   .3285182   1.015453     0.32   0.746    -1.661732    2.318769
         ldp |    .757831   .9715253     0.78   0.435    -1.146324    2.661986
     noparty |   3.206841   1.203018     2.67   0.008     .8489693    5.564712
     conserv |   .1052245   .2473144     0.43   0.670    -.3795029    .5899518
       _cons |  -10.82693   3.322676    -3.26   0.001    -17.33925     -4.3146
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b4

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_7 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -94.138347  
Iteration 1:   log likelihood = -77.270403  
Iteration 2:   log likelihood = -77.005824  
Iteration 3:   log likelihood = -77.005322  
Iteration 4:   log likelihood = -77.005322  

Logistic regression                             Number of obs     =        137
                                                LR chi2(16)       =      34.27
                                                Prob > chi2       =     0.0050
Log likelihood = -77.005322                     Pseudo R2         =     0.1820

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -.9224316   .7677214    -1.20   0.230    -2.427138    .5822747
  hist_japan |  -1.418875   .5292627    -2.68   0.007    -2.456211   -.3815396
hist_foreign |  -.1781568   .5050002    -0.35   0.724    -1.167939    .8116254
 nationalism |  -.0738359    .762465    -0.10   0.923     -1.56824    1.420568
         age |  -.0227713   .0175724    -1.30   0.195    -.0572125    .0116699
        male |   .5734159   .4729128     1.21   0.225    -.3534762    1.500308
     college |   1.195787   .4481233     2.67   0.008     .3174818    2.074093
    fulltime |   .0039322   .5044879     0.01   0.994    -.9848459    .9927103
    parttime |   -.321443   .6422831    -0.50   0.617    -1.580295    .9374086
      income |  -.0879775   .1672898    -0.53   0.599    -.4158595    .2399046
socialstatus |   .1383483   .1378806     1.00   0.316    -.1318927    .4085893
        news |   .0067573   .3376274     0.02   0.984    -.6549802    .6684948
     defense |  -1.038428   .4666637    -2.23   0.026    -1.953072   -.1237838
         ldp |  -.3665685   .6590067    -0.56   0.578    -1.658198    .9250609
     noparty |   .2976358   .5551526     0.54   0.592    -.7904432    1.385715
     conserv |   .0042059   .1304648     0.03   0.974    -.2515004    .2599122
       _cons |   .5326646   1.536788     0.35   0.729    -2.479384    3.544713
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b5

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_3 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -100.08562  
Iteration 1:   log likelihood = -86.830947  
Iteration 2:   log likelihood = -86.648286  
Iteration 3:   log likelihood = -86.647845  
Iteration 4:   log likelihood = -86.647845  

Logistic regression                             Number of obs     =        151
                                                LR chi2(16)       =      26.88
                                                Prob > chi2       =     0.0429
Log likelihood = -86.647845                     Pseudo R2         =     0.1343

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -.1904707   .7147434    -0.27   0.790    -1.591342    1.210401
  hist_japan |  -.5919933   .4846312    -1.22   0.222    -1.541853    .3578664
hist_foreign |  -.2587948   .4705775    -0.55   0.582     -1.18111    .6635202
 nationalism |   1.494036   .7626573     1.96   0.050    -.0007443    2.988817
         age |  -.0371587   .0165026    -2.25   0.024    -.0695032   -.0048143
        male |   .6005755   .4345053     1.38   0.167    -.2510392     1.45219
     college |  -.4708078   .4432456    -1.06   0.288    -1.339553    .3979376
    fulltime |  -.2592486   .4689769    -0.55   0.580    -1.178427    .6599293
    parttime |  -.2045248   .6219423    -0.33   0.742    -1.423509     1.01446
      income |    -.00491   .1585875    -0.03   0.975    -.3157358    .3059159
socialstatus |   -.169216   .1297028    -1.30   0.192    -.4234289    .0849969
        news |   .8215329   .3419036     2.40   0.016     .1514141    1.491652
     defense |   .1266908     .43537     0.29   0.771    -.7266186    .9800002
         ldp |   .9158839   .5954233     1.54   0.124    -.2511244    2.082892
     noparty |   .1023683   .5073426     0.20   0.840     -.892005    1.096742
     conserv |  -.0185521   .1144668    -0.16   0.871    -.2429028    .2057987
       _cons |  -1.190519   1.460295    -0.82   0.415    -4.052644    1.671606
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b6

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_4 > -1 & a14x_russia==1 & dup<2

Iteration 0:   log likelihood = -82.278843  
Iteration 1:   log likelihood = -69.742439  
Iteration 2:   log likelihood = -68.835044  
Iteration 3:   log likelihood = -68.832285  
Iteration 4:   log likelihood = -68.832285  

Logistic regression                             Number of obs     =        158
                                                LR chi2(16)       =      26.89
                                                Prob > chi2       =     0.0427
Log likelihood = -68.832285                     Pseudo R2         =     0.1634

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   -.255828   .9063378    -0.28   0.778    -2.032217    1.520561
  hist_japan |   .1872518   .5743399     0.33   0.744    -.9384338    1.312937
hist_foreign |   .0250272   .5724544     0.04   0.965    -1.096963    1.147017
 nationalism |   1.987256   .9472807     2.10   0.036     .1306198    3.843892
         age |  -.0617808   .0194323    -3.18   0.001    -.0998674   -.0236942
        male |    .982633   .5303745     1.85   0.064     -.056882    2.022148
     college |  -.4581381     .49523    -0.93   0.355    -1.428771    .5124949
    fulltime |  -.2041199   .5366079    -0.38   0.704    -1.255852    .8476123
    parttime |   .2227364   .7183027     0.31   0.756    -1.185111    1.630584
      income |  -.1679528   .1946441    -0.86   0.388    -.5494483    .2135427
socialstatus |   .0190062   .1519862     0.13   0.900    -.2788813    .3168938
        news |   .8299225   .3800685     2.18   0.029     .0850019    1.574843
     defense |   .0592647   .5029257     0.12   0.906    -.9264515    1.044981
         ldp |  -.0574186   .7157763    -0.08   0.936    -1.460314    1.345477
     noparty |    .349085   .6233272     0.56   0.575    -.8726138    1.570784
     conserv |   .1441257   .1379419     1.04   0.296    -.1262355    .4144869
       _cons |  -3.098248   1.658117    -1.87   0.062    -6.348097    .1516011
------------------------------------------------------------------------------

. outreg2 using tableA7, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA7.tex
tableA7.xml
dir : seeout

. estimates store b7

. 
. **Table 7 in Appendix
. seeout, lab
Hit Enter to continue. 

. 
. 
. **Thinking about South Korea only 
. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_1 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -64.457311  
Iteration 1:   log likelihood = -49.432346  
Iteration 2:   log likelihood = -49.326702  
Iteration 3:   log likelihood = -49.326334  
Iteration 4:   log likelihood = -49.326334  

Logistic regression                             Number of obs     =         93
                                                LR chi2(16)       =      30.26
                                                Prob > chi2       =     0.0167
Log likelihood = -49.326334                     Pseudo R2         =     0.2347

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |    .522965   .6751562     0.77   0.439     -.800317    1.846247
  hist_japan |   1.957568   .7165396     2.73   0.006     .5531763     3.36196
hist_foreign |  -.1133279   .6738335    -0.17   0.866    -1.434017    1.207361
 nationalism |     .70642    1.17646     0.60   0.548    -1.599399    3.012239
         age |   .0079044   .0223968     0.35   0.724    -.0359926    .0518013
        male |  -.2274228   .5717989    -0.40   0.691    -1.348128    .8932825
     college |   .5930369   .5699037     1.04   0.298    -.5239539    1.710028
    fulltime |  -.1862687   .6526283    -0.29   0.775    -1.465397    1.092859
    parttime |  -1.093519   .7946189    -1.38   0.169    -2.650944    .4639054
      income |   .0330854   .1902739     0.17   0.862    -.3398447    .4060154
socialstatus |  -.2575902   .1778163    -1.45   0.147    -.6061036    .0909233
        news |   -.210618    .493008    -0.43   0.669    -1.176896    .7556599
     defense |   .3686644    .632121     0.58   0.560      -.87027    1.607599
         ldp |   -1.02996   .8143448    -1.26   0.206    -2.626047    .5661261
     noparty |  -.4777381   .6730838    -0.71   0.478    -1.796958     .841482
     conserv |   .4453779   .1674262     2.66   0.008     .1172285    .7735273
       _cons |  -1.735958   2.129972    -0.82   0.415    -5.910626     2.43871
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c1

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_2 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -64.744664  
Iteration 1:   log likelihood = -38.101091  
Iteration 2:   log likelihood = -35.699701  
Iteration 3:   log likelihood = -35.577094  
Iteration 4:   log likelihood =  -35.57694  
Iteration 5:   log likelihood =  -35.57694  

Logistic regression                             Number of obs     =        100
                                                LR chi2(16)       =      58.34
                                                Prob > chi2       =     0.0000
Log likelihood =  -35.57694                     Pseudo R2         =     0.4505

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   1.996227   .7728144     2.58   0.010     .4815385    3.510915
  hist_japan |   .9920556   .8247722     1.20   0.229    -.6244683    2.608579
hist_foreign |  -1.949379   .9364903    -2.08   0.037    -3.784866   -.1138915
 nationalism |   3.851624   1.717059     2.24   0.025     .4862503    7.216999
         age |  -.1098511   .0353674    -3.11   0.002    -.1791699   -.0405323
        male |   1.302613   .8986344     1.45   0.147    -.4586785    3.063904
     college |   .0003064   .7014204     0.00   1.000    -1.374452    1.375065
    fulltime |  -.6899896   .8884475    -0.78   0.437    -2.431315    1.051335
    parttime |  -2.347905   1.206857    -1.95   0.052    -4.713302    .0174918
      income |   .1298084   .2325824     0.56   0.577    -.3260448    .5856616
socialstatus |  -.6220105   .2611282    -2.38   0.017    -1.133812   -.1102087
        news |   1.171318   .6016582     1.95   0.052    -.0079104    2.350546
     defense |   .7077842   .7472499     0.95   0.344    -.7567988    2.172367
         ldp |  -.4734602   .8540761    -0.55   0.579    -2.147419    1.200498
     noparty |   1.230778   .9227718     1.33   0.182    -.5778215    3.039377
     conserv |   .4318834   .2004851     2.15   0.031     .0389398    .8248269
       _cons |  -2.428982   2.414822    -1.01   0.314    -7.161946    2.303981
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c2

. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_5 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -53.899132  
Iteration 1:   log likelihood = -30.989399  
Iteration 2:   log likelihood = -23.550254  
Iteration 3:   log likelihood = -22.924213  
Iteration 4:   log likelihood =  -22.90021  
Iteration 5:   log likelihood = -22.900132  
Iteration 6:   log likelihood = -22.900132  

Logistic regression                             Number of obs     =        105
                                                LR chi2(16)       =      62.00
                                                Prob > chi2       =     0.0000
Log likelihood = -22.900132                     Pseudo R2         =     0.5751

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   -3.11636   1.136696    -2.74   0.006    -5.344243    -.888477
  hist_japan |  -2.325436   1.463178    -1.59   0.112    -5.193213    .5423411
hist_foreign |  -2.118953   1.519723    -1.39   0.163    -5.097556    .8596493
 nationalism |   1.177409   1.881914     0.63   0.532    -2.511074    4.865892
         age |   .1175755   .0437306     2.69   0.007     .0318651    .2032858
        male |   .3783114   1.108425     0.34   0.733    -1.794161    2.550784
     college |   1.339817   .9544497     1.40   0.160      -.53087    3.210504
    fulltime |  -1.638105   1.286357    -1.27   0.203    -4.159319    .8831093
    parttime |  -.4800905   1.445143    -0.33   0.740    -3.312519    2.352338
      income |  -.3476722   .3521884    -0.99   0.324    -1.037949    .3426044
socialstatus |  -.5543246   .4160394    -1.33   0.183    -1.369747    .2610976
        news |  -1.261946   .7983142    -1.58   0.114    -2.826613    .3027212
     defense |  -.7945712   .9962057    -0.80   0.425    -2.747098    1.157956
         ldp |  -3.738174   1.609848    -2.32   0.020    -6.893417   -.5829301
     noparty |  -3.197454   1.721037    -1.86   0.063    -6.570625    .1757176
     conserv |  -.6380879   .2680819    -2.38   0.017    -1.163519   -.1126571
       _cons |   12.93984   4.776715     2.71   0.007     3.577652    22.30203
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c3

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_6 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -25.786308  
Iteration 1:   log likelihood = -19.238394  
Iteration 2:   log likelihood = -14.454808  
Iteration 3:   log likelihood = -13.380111  
Iteration 4:   log likelihood = -13.045125  
Iteration 5:   log likelihood =  -12.97224  
Iteration 6:   log likelihood = -12.969087  
Iteration 7:   log likelihood = -12.969071  
Iteration 8:   log likelihood = -12.969071  

Logistic regression                             Number of obs     =        106
                                                LR chi2(16)       =      25.63
                                                Prob > chi2       =     0.0594
Log likelihood = -12.969071                     Pseudo R2         =     0.4971

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -7.382804   6.146642    -1.20   0.230       -19.43    4.664392
  hist_japan |  -2.330873   2.802392    -0.83   0.406    -7.823461    3.161715
hist_foreign |  -2.181763   2.322125    -0.94   0.347    -6.733044    2.369518
 nationalism |   7.734726   5.313508     1.46   0.145    -2.679559    18.14901
         age |    -.10743   .0852089    -1.26   0.207    -.2744363    .0595763
        male |   4.088508   3.136869     1.30   0.192    -2.059642    10.23666
     college |  -5.982988   5.280332    -1.13   0.257    -16.33225    4.366272
    fulltime |   4.583002   5.489267     0.83   0.404    -6.175765    15.34177
    parttime |   .2002119   2.991933     0.07   0.947    -5.663869    6.064293
      income |  -.6552216   .6513971    -1.01   0.314    -1.931936    .6214932
socialstatus |  -1.677484   1.831749    -0.92   0.360    -5.267646    1.912678
        news |  -.2300146   1.651385    -0.14   0.889     -3.46667    3.006641
     defense |   4.008905    3.88754     1.03   0.302    -3.610532    11.62834
         ldp |    5.18814   3.603496     1.44   0.150    -1.874581    12.25086
     noparty |   7.394272   5.428646     1.36   0.173     -3.24568    18.03422
     conserv |  -1.918081   1.572825    -1.22   0.223    -5.000761    1.164599
       _cons |   29.00436   25.24802     1.15   0.251    -20.48085    78.48956
------------------------------------------------------------------------------
Note: 0 failures and 17 successes completely determined.

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c4

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_7 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -57.227132  
Iteration 1:   log likelihood = -38.542554  
Iteration 2:   log likelihood = -36.805106  
Iteration 3:   log likelihood = -36.749283  
Iteration 4:   log likelihood = -36.749194  
Iteration 5:   log likelihood = -36.749194  

Logistic regression                             Number of obs     =         87
                                                LR chi2(16)       =      40.96
                                                Prob > chi2       =     0.0006
Log likelihood = -36.749194                     Pseudo R2         =     0.3578

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -3.748013   1.244444    -3.01   0.003    -6.187078   -1.308948
  hist_japan |  -.9178929   .8298566    -1.11   0.269    -2.544382    .7085962
hist_foreign |  -1.033957   .8051672    -1.28   0.199    -2.612056    .5441419
 nationalism |  -1.055549   1.147415    -0.92   0.358     -3.30444    1.193343
         age |   .0818126   .0318294     2.57   0.010     .0194281    .1441971
        male |  -.5013475   .6832909    -0.73   0.463    -1.840573    .8378782
     college |   .0329807   .6435725     0.05   0.959    -1.228398     1.29436
    fulltime |  -.3092466   .7331353    -0.42   0.673    -1.746165    1.127672
    parttime |  -.4989607   .9107259    -0.55   0.584    -2.283951    1.286029
      income |  -.3680421    .236246    -1.56   0.119    -.8310758    .0949916
socialstatus |   .3059297   .2113896     1.45   0.148    -.1083863    .7202457
        news |  -.4253645   .5710278    -0.74   0.456    -1.544558    .6938293
     defense |   1.023984   .7673919     1.33   0.182    -.4800762    2.528045
         ldp |  -1.290607   1.009359    -1.28   0.201    -3.268915    .6876998
     noparty |  -.0343489    .783294    -0.04   0.965    -1.569577    1.500879
     conserv |  -.1805553   .1799914    -1.00   0.316    -.5333321    .1722214
       _cons |  -.2729585   2.474831    -0.11   0.912    -5.123537    4.577621
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c5

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_3 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -65.202554  
Iteration 1:   log likelihood =  -48.96382  
Iteration 2:   log likelihood = -48.765707  
Iteration 3:   log likelihood = -48.765399  
Iteration 4:   log likelihood = -48.765399  

Logistic regression                             Number of obs     =         96
                                                LR chi2(16)       =      32.87
                                                Prob > chi2       =     0.0077
Log likelihood = -48.765399                     Pseudo R2         =     0.2521

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   1.378116   .6278087     2.20   0.028      .147634    2.608599
  hist_japan |   .4260031   .6711988     0.63   0.526    -.8895224    1.741528
hist_foreign |   -.660154   .7354165    -0.90   0.369    -2.101544    .7812358
 nationalism |   1.552962   1.101684     1.41   0.159    -.6062997    3.712224
         age |  -.0563844   .0237924    -2.37   0.018    -.1030166   -.0097522
        male |   .8100514   .6533378     1.24   0.215    -.4704672     2.09057
     college |   .1981729   .5662616     0.35   0.726    -.9116795    1.308025
    fulltime |   .0641455   .6560124     0.10   0.922    -1.221615    1.349906
    parttime |  -.8890159   .8575272    -1.04   0.300    -2.569738    .7917064
      income |  -.1204952   .2006909    -0.60   0.548    -.5138422    .2728518
socialstatus |  -.1185516   .1710982    -0.69   0.488     -.453898    .2167948
        news |   .4391427   .4655092     0.94   0.345    -.4732385    1.351524
     defense |   .4133315   .6117846     0.68   0.499    -.7857443    1.612407
         ldp |   .4761369   .7181707     0.66   0.507    -.9314517    1.883726
     noparty |   .6828118   .7314904     0.93   0.351     -.750883    2.116507
     conserv |   .0823752   .1488867     0.55   0.580    -.2094373    .3741878
       _cons |  -.8246554     1.9214    -0.43   0.668    -4.590531     2.94122
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c6

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism age male college fulltime parttime income socialstatus news defense ld
> p noparty conserv if q100_4 > -1 & a14x_southkorea==1 & dup<2

Iteration 0:   log likelihood = -61.553466  
Iteration 1:   log likelihood = -45.161222  
Iteration 2:   log likelihood = -43.960348  
Iteration 3:   log likelihood = -43.941771  
Iteration 4:   log likelihood = -43.941758  
Iteration 5:   log likelihood = -43.941758  

Logistic regression                             Number of obs     =        104
                                                LR chi2(16)       =      35.22
                                                Prob > chi2       =     0.0037
Log likelihood = -43.941758                     Pseudo R2         =     0.2861

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   1.211008    .632205     1.92   0.055    -.0280908    2.450107
  hist_japan |   -.478061    .750167    -0.64   0.524    -1.948361    .9922393
hist_foreign |  -.6074388   .7909855    -0.77   0.443    -2.157742    .9428643
 nationalism |   1.145772   1.241383     0.92   0.356    -1.287294    3.578838
         age |  -.0495069   .0273422    -1.81   0.070    -.1030965    .0040828
        male |   .6204745    .663081     0.94   0.349    -.6791405    1.920089
     college |   -.986956   .6902724    -1.43   0.153    -2.339865    .3659531
    fulltime |   1.198469   .7155595     1.67   0.094    -.2040015     2.60094
    parttime |  -.3827486   1.077091    -0.36   0.722    -2.493809    1.728312
      income |   .0081508   .2151283     0.04   0.970     -.413493    .4297946
socialstatus |  -.1065532   .1899015    -0.56   0.575    -.4787532    .2656469
        news |  -.0986596   .4954229    -0.20   0.842    -1.069671    .8723515
     defense |   1.689756   .6742012     2.51   0.012      .368346    3.011166
         ldp |  -.0553864   .7716706    -0.07   0.943    -1.567833     1.45706
     noparty |   .4788775   .7449042     0.64   0.520    -.9811079    1.938863
     conserv |   .0716691    .153462     0.47   0.640    -.2291109    .3724492
       _cons |  -.1396533   2.137034    -0.07   0.948    -4.328164    4.048857
------------------------------------------------------------------------------

. outreg2 using tableA8, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA8.tex
tableA8.xml
dir : seeout

. estimates store c7

. 
. **Table 8 in Appendix
. seeout, lab
Hit Enter to continue. 

. 
. 
. **Figure 6 in main text
. **only plotting coefficient of indivisibility     
. coefplot (a5, label(Senkaku/Diaoyu Islands Dispute with China) m(Oh)) /// 
>                 (b5, label(Northern Territories/Kurile Islands Dispute with Russia) m(Sh)) ///
>                 (c5, label(Takeshima/Dokdo Islands Dispute with South Korea) m(Th)), bylabel(Shelving the Dispute) ///
>       || a3 b3 c3, bylabel(Bilateral Negotiation) ///
>           || a1 b1 c1, bylabel(Publicity) ///
>           || a4 b4 c4, bylabel(IO Arbitration) ///
>           || a2 b2 c2, bylabel(Economic Sanction) ///
>           || a6 b6 c6, bylabel(Limited Military) ///
>           || a7 b7 c7, bylabel(Full Military) ///
>           ||, keep(hardcore) xline(0) legend(col(1) pos(6)) xlabel(-15(5)5) xscale(range(-15(5)5)) scheme(plotplain)

. 
. graph export figure6.pdf, replace
(file figure6.pdf written in PDF format)

. 
. 
. // Testing H1 with the treatment of historical ownership ("unsure" combined with "unacceptable)
. 
. **Appendix Figure 3： Historical ownership treatment
. 
. preserve

. recode a01 a02 a03a a03b a04 (3 = 0)
(a01: 534 changes made)
(a02: 572 changes made)
(a03a: 303 changes made)
(a03b: 309 changes made)
(a04: 687 changes made)

. mean a01 if a01 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        864

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |   .1990741   .0135925      .1723959    .2257522
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat1 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a01 if a01 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        912

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |   .3399123   .0156937      .3091123    .3707123
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat2 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a01 if a01 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        845

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a01 |    .347929   .0163954      .3157485    .3801095
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat3 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        864

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .5810185   .0167953      .5480542    .6139829
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat4 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        912

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |    .433114   .0164168      .4008948    .4653333
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat5 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a04 if a04 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        845

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a04 |   .4745562   .0171884      .4408192    .5082933
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat6 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        864

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |   .3726852   .0164592      .3403805    .4049899
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat7 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        912

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |   .3618421   .0159208      .3305964    .3930878
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat8 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a02 if a02 < 2 & hist_none == 1

Mean estimation                   Number of obs   =        845

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         a02 |   .3786982   .0166965      .3459266    .4114699
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat9 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        453

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |    .388521    .022926      .3434661    .4335758
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat10 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        475

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |   .4484211   .0228432      .4035345    .4933076
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat11 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03b if a03b < 2 & hist_none == 1

Mean estimation                   Number of obs   =        423

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03b |   .4468085   .0242015       .399238     .494379
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat12 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_japan == 1

Mean estimation                   Number of obs   =        411

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |   .4184915   .0243629      .3705997    .4663833
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat13 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_foreign == 1

Mean estimation                   Number of obs   =        437

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |   .4416476    .023782      .3949059    .4883893
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat14 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. mean a03a if a03a < 2 & hist_none == 1

Mean estimation                   Number of obs   =        422

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        a03a |   .3981043   .0238571      .3512104    .4449982
--------------------------------------------------------------

. matrix mean = e(b)

. matrix var = e(V)

. matrix mat15 = (mean[1,1], mean[1,1]-1.96*(sqrt(var[1,1])), mean[1,1]+1.96*(sqrt(var[1,1])))

. 
. 
. matrix t1 = mat1\mat7\mat10\mat13\mat4

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat8\mat11\mat14\mat5

. matrix rownames t2 = a c d e b

. 
. matrix t3 = mat3\mat9\mat12\mat15\mat6

. matrix rownames t3 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Historically Owned by Japan) m(Oh)) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Historically Owned by Foreign) m(Sh))  ///
>  (matrix(t3[.,1]), ci("t3[.,2] t3[.,3]") label(Historically Owned by Neither) m(Th)),  ///
>  coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6))  ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. graph export figureA3.pdf, replace
(file figureA3.pdf written in PDF format)

. restore

. 
. **Appendix Figure 4: looking at military strength of the neighboring country
. 
. preserve

. recode a01 a02 a03a a03b a04 (3 = 0)
(a01: 534 changes made)
(a02: 572 changes made)
(a03a: 303 changes made)
(a03b: 309 changes made)
(a04: 687 changes made)

. 
. ttest a01 if a01 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,286    .2877138    .0126286    .4528731    .2629389    .3124888
       1 |   1,335    .3041199    .0125954    .4602061    .2794109    .3288288
---------+--------------------------------------------------------------------
combined |   2,621    .2960702    .0089189    .4566094    .2785814     .313559
---------+--------------------------------------------------------------------
    diff |            -.016406    .0178414               -.0513908    .0185788
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.9195
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1789         Pr(|T| > |t|) = 0.3579          Pr(T > t) = 0.8211

. matrix mat1 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat2 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a04 if a04 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,286    .4603421    .0139043    .4986187    .4330646    .4876197
       1 |   1,335     .528839    .0136669    .4993547    .5020281    .5556498
---------+--------------------------------------------------------------------
combined |   2,621    .4952308    .0097679    .5000727    .4760773    .5143843
---------+--------------------------------------------------------------------
    diff |           -.0684968     .019497               -.1067279   -.0302657
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.5132
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0002         Pr(|T| > |t|) = 0.0005          Pr(T > t) = 0.9998

. matrix mat3 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat4 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a02 if a02 < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,286    .3600311    .0133905    .4801958    .3337614    .3863008
       1 |   1,335    .3812734    .0132981    .4858815    .3551859    .4073609
---------+--------------------------------------------------------------------
combined |   2,621    .3708508    .0094368    .4831248    .3523464    .3893552
---------+--------------------------------------------------------------------
    diff |           -.0212423     .018876               -.0582557    .0157711
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.1254
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1303         Pr(|T| > |t|) = 0.2605          Pr(T > t) = 0.8697

. matrix mat5 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat6 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03b if a03b < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     663    .4313725    .0192491    .4956418    .3935758    .4691693
       1 |     688    .4244186     .018857     .494614    .3873944    .4614428
---------+--------------------------------------------------------------------
combined |   1,351    .4278312    .0134658    .4949475    .4014151    .4542474
---------+--------------------------------------------------------------------
    diff |            .0069539    .0269455               -.0459057    .0598135
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.2581
Ho: diff = 0                                     degrees of freedom =     1349

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6018         Pr(|T| > |t|) = 0.7964          Pr(T > t) = 0.3982

. matrix mat7 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat8 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03a if a03a < 2, by (powerful) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     623    .4317817    .0198607    .4957224    .3927796    .4707838
       1 |     647    .4080371    .0193366    .4918503    .3700669    .4460073
---------+--------------------------------------------------------------------
combined |   1,270     .419685    .0138536    .4937018    .3925065    .4468635
---------+--------------------------------------------------------------------
    diff |            .0237446    .0277151               -.0306278     .078117
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.8567
Ho: diff = 0                                     degrees of freedom =     1268

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8041         Pr(|T| > |t|) = 0.3918          Pr(T > t) = 0.1959

. matrix mat9 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat10 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. matrix t1 = mat1\mat5\mat7\mat9\mat3

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat6\mat8\mat10\mat4

. matrix rownames t2 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Militarily Weak) m(Oh) ) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Militarily Strong) m(Sh)),  ///
> coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6)) ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. 
. graph export figureA4.pdf, replace
(file figureA4.pdf written in PDF format)

. restore

. 
. **Appendix Figure 5: looking at value of the territory
. 
. preserve

. recode a01 a02 a03a a03b a04 (3 = 0)
(a01: 534 changes made)
(a02: 572 changes made)
(a03a: 303 changes made)
(a03b: 309 changes made)
(a04: 687 changes made)

. 
. ttest a01 if a01 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,279    .2900704    .0126939    .4539721    .2651673    .3149735
       1 |   1,342    .3017884    .0125352    .4592051    .2771977    .3263791
---------+--------------------------------------------------------------------
combined |   2,621    .2960702    .0089189    .4566094    .2785814     .313559
---------+--------------------------------------------------------------------
    diff |            -.011718    .0178449               -.0467095    .0232735
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6567
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2557         Pr(|T| > |t|) = 0.5115          Pr(T > t) = 0.7443

. matrix mat1 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat2 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a04 if a04 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,279    .4956998    .0139858    .5001771     .468262    .5231375
       1 |   1,342    .4947839    .0136531    .5001592    .4680001    .5215677
---------+--------------------------------------------------------------------
combined |   2,621    .4952308    .0097679    .5000727    .4760773    .5143843
---------+--------------------------------------------------------------------
    diff |            .0009159    .0195451               -.0374095    .0392413
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0469
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5187         Pr(|T| > |t|) = 0.9626          Pr(T > t) = 0.4813

. matrix mat3 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat4 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a02 if a02 < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,279    .3682565    .0134921    .4825201    .3417873    .3947256
       1 |   1,342    .3733234    .0132084    .4838672     .347412    .3992348
---------+--------------------------------------------------------------------
combined |   2,621    .3708508    .0094368    .4831248    .3523464    .3893552
---------+--------------------------------------------------------------------
    diff |           -.0050669    .0188824                -.042093    .0319591
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.2683
Ho: diff = 0                                     degrees of freedom =     2619

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3942         Pr(|T| > |t|) = 0.7885          Pr(T > t) = 0.6058

. matrix mat5 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat6 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03b if a03b < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     648    .4135802    .0193612    .4928554    .3755619    .4515986
       1 |     703    .4409673    .0187393    .4968564    .4041755    .4777591
---------+--------------------------------------------------------------------
combined |   1,351    .4278312    .0134658    .4949475    .4014151    .4542474
---------+--------------------------------------------------------------------
    diff |            -.027387    .0269536               -.0802625    .0254884
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.0161
Ho: diff = 0                                     degrees of freedom =     1349

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1549         Pr(|T| > |t|) = 0.3098          Pr(T > t) = 0.8451

. matrix mat7 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat8 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. ttest a03a if a03a < 2, by (valuable) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     631    .4057052     .019563    .4914175    .3672886    .4441219
       1 |     639    .4334898    .0196193    .4959449    .3949637     .472016
---------+--------------------------------------------------------------------
combined |   1,270     .419685    .0138536    .4937018    .3925065    .4468635
---------+--------------------------------------------------------------------
    diff |           -.0277846    .0277077               -.0821426    .0265734
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.0028
Ho: diff = 0                                     degrees of freedom =     1268

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1581         Pr(|T| > |t|) = 0.3162          Pr(T > t) = 0.8419

. matrix mat9 = (r(mu_1), r(mu_1)-1.96*(r(sd_1)/sqrt(r(N_1))), r(mu_1)+1.96*(r(sd_1)/sqrt(r(N_1))))

. matrix mat10 = (r(mu_2), r(mu_2)-1.96*(r(sd_2)/sqrt(r(N_2))), r(mu_2)+1.96*(r(sd_2)/sqrt(r(N_2))))

. 
. matrix t1 = mat1\mat5\mat7\mat9\mat3

. matrix rownames t1 = a c d e b

. 
. matrix t2 = mat2\mat6\mat8\mat10\mat4

. matrix rownames t2 = a c d e b

. 
. coefplot (matrix(t1[.,1]), ci("t1[.,2] t1[.,3]") label(Value Unsure) m(Oh) ) ///
>  (matrix(t2[.,1]), ci("t2[.,2] t2[.,3]") label(Valuable) m(Sh)),  ///
>  coeflabel(a = `" "Sharing Sovereignty" "and Right to Use" "' ///
>                    b = `" "Japan has Sovereignty" "and Right to Use" "' ///
>            c = `" "Japan has Sovereignty" "but Co-development" "' ///
>            d = `" "Side-Payment with IO" "' ///
>            e = `" "Side-Payment without IO" "', labsize(vsmall) ) ///
> mlabel format(%9.2g) mlabposition(1) legend(row(1) pos(6)) ///
> ytitle(Proportion of Accepting the Outcome) vertical scheme(plotplain)

. 
. graph export figureA5.pdf, replace
(file figureA5.pdf written in PDF format)

. restore                 

.                         
.                         
. //Appendix Table 3:  Using the IRT Measure of Indivisibility
. 
. logit q100_1 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_1 > -1

Iteration 0:   log likelihood = -1237.5622  
Iteration 1:   log likelihood =  -1116.507  
Iteration 2:   log likelihood = -1116.3167  
Iteration 3:   log likelihood = -1116.3167  

Logistic regression                             Number of obs     =      1,801
                                                LR chi2(18)       =     242.49
                                                Prob > chi2       =     0.0000
Log likelihood = -1116.3167                     Pseudo R2         =     0.0980

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |   .4003058    .068275     5.86   0.000     .2664893    .5341222
  hist_japan |   .3371747   .1256359     2.68   0.007     .0909328    .5834165
hist_foreign |  -.3817692   .1259175    -3.03   0.002    -.6285631   -.1349754
 nationalism |   1.104372   .1945969     5.68   0.000     .7229693    1.485775
    powerful |   .2149097   .1019061     2.11   0.035     .0151774     .414642
    valuable |    .222286   .1021965     2.18   0.030     .0219845    .4225876
         age |   .0007597   .0041244     0.18   0.854    -.0073241    .0088434
        male |   .5201905   .1181513     4.40   0.000     .2886182    .7517629
     college |  -.0738334   .1138953    -0.65   0.517     -.297064    .1493973
    fulltime |   .0028615   .1239609     0.02   0.982    -.2400974    .2458204
    parttime |   -.041167    .162362    -0.25   0.800    -.3593907    .2770567
      income |   .0256528   .0399039     0.64   0.520    -.0525574    .1038629
socialstatus |  -.0691964   .0339388    -2.04   0.041    -.1357152   -.0026776
        news |   .2244548   .0818507     2.74   0.006     .0640304    .3848793
     defense |   .4972416    .108113     4.60   0.000      .285344    .7091391
         ldp |   .0922444   .1491685     0.62   0.536    -.2001206    .3846093
     noparty |  -.0114254   .1269638    -0.09   0.928      -.26027    .2374191
     conserv |    .060714   .0276864     2.19   0.028     .0064496    .1149784
       _cons |   -2.40667   .3765892    -6.39   0.000    -3.144771   -1.668568
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_2 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_5 > -1

Iteration 0:   log likelihood = -1276.4469  
Iteration 1:   log likelihood = -1206.7183  
Iteration 2:   log likelihood = -1206.3162  
Iteration 3:   log likelihood = -1206.3161  

Logistic regression                             Number of obs     =      1,894
                                                LR chi2(18)       =     140.26
                                                Prob > chi2       =     0.0000
Log likelihood = -1206.3161                     Pseudo R2         =     0.0549

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |   .3939195    .064468     6.11   0.000     .2675645    .5202744
  hist_japan |   .1643526   .1205965     1.36   0.173    -.0720121    .4007174
hist_foreign |   -.169755   .1203031    -1.41   0.158    -.4055448    .0660348
 nationalism |   .4605223   .1820171     2.53   0.011     .1037753    .8172693
    powerful |   .0521646   .0977866     0.53   0.594    -.1394937    .2438228
    valuable |   .0687993   .0979737     0.70   0.483    -.1232256    .2608242
         age |  -.0195443   .0039638    -4.93   0.000    -.0273132   -.0117754
        male |    .374271   .1127584     3.32   0.001     .1532687    .5952733
     college |  -.1374227    .110095    -1.25   0.212    -.3532049    .0783595
    fulltime |   .0113957   .1184927     0.10   0.923    -.2208457    .2436371
    parttime |   .0727495   .1562658     0.47   0.642    -.2335258    .3790247
      income |   .0092709   .0382286     0.24   0.808    -.0656559    .0841977
socialstatus |  -.0593387   .0320724    -1.85   0.064    -.1221994    .0035219
        news |   -.001996   .0770407    -0.03   0.979    -.1529929     .149001
     defense |   .2921125   .1036587     2.82   0.005     .0889452    .4952799
         ldp |   .1359163   .1432536     0.95   0.343    -.1448556    .4166881
     noparty |   .0229757    .122591     0.19   0.851    -.2172983    .2632497
     conserv |   .0648922   .0267607     2.42   0.015     .0124423    .1173421
       _cons |  -.2071596   .3551474    -0.58   0.560    -.9032357    .4889164
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_5 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_7 > -1

Iteration 0:   log likelihood = -923.81065  
Iteration 1:   log likelihood = -798.70727  
Iteration 2:   log likelihood = -789.41985  
Iteration 3:   log likelihood = -789.38761  
Iteration 4:   log likelihood = -789.38761  

Logistic regression                             Number of obs     =      1,732
                                                LR chi2(18)       =     268.85
                                                Prob > chi2       =     0.0000
Log likelihood = -789.38761                     Pseudo R2         =     0.1455

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |  -1.202474   .0962064   -12.50   0.000    -1.391035   -1.013913
  hist_japan |  -.2810232    .154234    -1.82   0.068    -.5833163    .0212699
hist_foreign |  -.0792108   .1613511    -0.49   0.623    -.3954531    .2370315
 nationalism |  -.1298705   .2296617    -0.57   0.572    -.5799991    .3202581
    powerful |   .1250233   .1257288     0.99   0.320    -.1214006    .3714471
    valuable |  -.3150898   .1262364    -2.50   0.013    -.5625085    -.067671
         age |   .0144966   .0050751     2.86   0.004     .0045495    .0244437
        male |  -.3787057   .1469753    -2.58   0.010     -.666772   -.0906394
     college |   .1479387   .1390185     1.06   0.287    -.1245326    .4204101
    fulltime |  -.2121717   .1524243    -1.39   0.164    -.5109179    .0865745
    parttime |  -.0870258   .2048499    -0.42   0.671    -.4885242    .3144726
      income |   .0915833   .0488111     1.88   0.061    -.0040847    .1872513
socialstatus |  -.0189159   .0401236    -0.47   0.637    -.0975568     .059725
        news |  -.0200477   .0973907    -0.21   0.837      -.21093    .1708346
     defense |  -.3394064   .1299005    -2.61   0.009    -.5940067   -.0848062
         ldp |  -.2738969   .1848041    -1.48   0.138    -.6361063    .0883124
     noparty |  -.2724445   .1630625    -1.67   0.095    -.5920411    .0471521
     conserv |  -.0094296   .0335985    -0.28   0.779    -.0752815    .0564222
       _cons |   1.647165     .45714     3.60   0.000     .7511867    2.543142
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_6 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_6 > -1

Iteration 0:   log likelihood = -666.71874  
Iteration 1:   log likelihood = -628.19671  
Iteration 2:   log likelihood = -625.14387  
Iteration 3:   log likelihood = -625.13544  
Iteration 4:   log likelihood = -625.13544  

Logistic regression                             Number of obs     =      2,012
                                                LR chi2(18)       =      83.17
                                                Prob > chi2       =     0.0000
Log likelihood = -625.13544                     Pseudo R2         =     0.0624

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |  -.4804686   .1039341    -4.62   0.000    -.6841756   -.2767616
  hist_japan |   .4517607   .1917088     2.36   0.018     .0760183     .827503
hist_foreign |   .0644561   .1798433     0.36   0.720    -.2880303    .4169426
 nationalism |   .9045997   .2593501     3.49   0.000     .3962828    1.412917
    powerful |   .4087667    .151973     2.69   0.007     .1109051    .7066283
    valuable |  -.0053147   .1511863    -0.04   0.972    -.3016343    .2910049
         age |   .0218075   .0060631     3.60   0.000     .0099241    .0336909
        male |  -.0838735    .170783    -0.49   0.623     -.418602    .2508549
     college |   .0392249   .1703537     0.23   0.818    -.2946623    .3731121
    fulltime |   .0295411   .1762778     0.17   0.867    -.3159571    .3750393
    parttime |   .4985667   .2734663     1.82   0.068    -.0374174    1.034551
      income |   .0705215   .0578539     1.22   0.223      -.04287     .183913
socialstatus |  -.1106132   .0491266    -2.25   0.024    -.2068995   -.0143269
        news |   .2290035    .114229     2.00   0.045     .0051189    .4528882
     defense |  -.1346442   .1588549    -0.85   0.397    -.4459942    .1767057
         ldp |   .4373266   .2167835     2.02   0.044     .0124387    .8622145
     noparty |   .3708294   .1830387     2.03   0.043     .0120801    .7295787
     conserv |   -.036189   .0409035    -0.88   0.376    -.1163585    .0439804
       _cons |  -.1550761   .5376452    -0.29   0.773    -1.208841    .8986892
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_7 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_2 > -1

Iteration 0:   log likelihood = -1285.1408  
Iteration 1:   log likelihood = -1107.0814  
Iteration 2:   log likelihood = -1106.8819  
Iteration 3:   log likelihood = -1106.8819  

Logistic regression                             Number of obs     =      1,855
                                                LR chi2(18)       =     356.52
                                                Prob > chi2       =     0.0000
Log likelihood = -1106.8819                     Pseudo R2         =     0.1387

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |   -1.00075   .0695515   -14.39   0.000    -1.137069   -.8644317
  hist_japan |  -.5339833   .1285174    -4.15   0.000    -.7858729   -.2820938
hist_foreign |  -.0847742   .1263895    -0.67   0.502    -.3324931    .1629448
 nationalism |  -.1471183   .1931161    -0.76   0.446    -.5256189    .2313823
    powerful |    -.02659   .1031045    -0.26   0.796     -.228671     .175491
    valuable |  -.0236175   .1036993    -0.23   0.820    -.2268643    .1796293
         age |   .0062566   .0041354     1.51   0.130    -.0018486    .0143617
        male |  -.2070407   .1190606    -1.74   0.082    -.4403952    .0263139
     college |   .1818023   .1156493     1.57   0.116    -.0448662    .4084707
    fulltime |  -.1010357   .1237843    -0.82   0.414    -.3436485    .1415771
    parttime |  -.1002851   .1662675    -0.60   0.546    -.4261633    .2255931
      income |  -.0268923   .0399202    -0.67   0.501    -.1051346    .0513499
socialstatus |   .0414917   .0336657     1.23   0.218    -.0244919    .1074752
        news |  -.1467003   .0809432    -1.81   0.070     -.305346    .0119455
     defense |  -.4763724   .1095769    -4.35   0.000    -.6911392   -.2616056
         ldp |  -.1829724    .151581    -1.21   0.227    -.4800656    .1141209
     noparty |  -.1928908   .1287708    -1.50   0.134    -.4452769    .0594954
     conserv |  -.0711385   .0282335    -2.52   0.012    -.1264751   -.0158019
       _cons |   1.140682   .3746982     3.04   0.002     .4062871    1.875077
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_3 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_3 > -1

Iteration 0:   log likelihood = -1315.1729  
Iteration 1:   log likelihood = -1161.8292  
Iteration 2:   log likelihood = -1161.6372  
Iteration 3:   log likelihood = -1161.6372  

Logistic regression                             Number of obs     =      1,904
                                                LR chi2(18)       =     307.07
                                                Prob > chi2       =     0.0000
Log likelihood = -1161.6372                     Pseudo R2         =     0.1167

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |   .4164139   .0662095     6.29   0.000     .2866457     .546182
  hist_japan |   .0452317   .1242855     0.36   0.716    -.1983634    .2888268
hist_foreign |  -.3988145   .1237095    -3.22   0.001    -.6412806   -.1563483
 nationalism |   1.086195    .188409     5.77   0.000     .7169197     1.45547
    powerful |   .2472174   .1003136     2.46   0.014     .0506063    .4438285
    valuable |   .0501849     .10034     0.50   0.617    -.1464779    .2468476
         age |  -.0268994   .0040705    -6.61   0.000    -.0348773   -.0189214
        male |   .3943442   .1159627     3.40   0.001     .1670615    .6216269
     college |  -.0983475   .1123041    -0.88   0.381    -.3184596    .1217645
    fulltime |    .104671   .1211449     0.86   0.388    -.1327688    .3421107
    parttime |   .1377834   .1598409     0.86   0.389     -.175499    .4510658
      income |    .041069   .0388765     1.06   0.291    -.0351275    .1172656
socialstatus |   -.070676   .0327552    -2.16   0.031     -.134875    -.006477
        news |   .1612673   .0808962     1.99   0.046     .0027137     .319821
     defense |    .566807   .1059482     5.35   0.000     .3591523    .7744617
         ldp |   .5116345   .1449345     3.53   0.000     .2275681    .7957008
     noparty |   .0591572   .1251667     0.47   0.636    -.1861652    .3044795
     conserv |   .0763468   .0270901     2.82   0.005     .0232512    .1294424
       _cons |  -.9018454   .3717151    -2.43   0.015    -1.630394   -.1732973
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. logit q100_4 hardcore4 hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialsta
> tus news defense ldp noparty conserv  if q100_4 > -1

Iteration 0:   log likelihood = -1134.6393  
Iteration 1:   log likelihood = -994.79588  
Iteration 2:   log likelihood = -988.94074  
Iteration 3:   log likelihood = -988.92933  
Iteration 4:   log likelihood = -988.92933  

Logistic regression                             Number of obs     =      1,969
                                                LR chi2(18)       =     291.42
                                                Prob > chi2       =     0.0000
Log likelihood = -988.92933                     Pseudo R2         =     0.1284

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   hardcore4 |   .3883643   .0735599     5.28   0.000     .2441896    .5325389
  hist_japan |  -.1140631   .1371331    -0.83   0.406     -.382839    .1547128
hist_foreign |  -.2183208   .1381167    -1.58   0.114    -.4890246     .052383
 nationalism |   1.047852   .2154342     4.86   0.000     .6256091    1.470096
    powerful |  -.0684923   .1115464    -0.61   0.539    -.2871191    .1501346
    valuable |   .0084663   .1117137     0.08   0.940    -.2104886    .2274212
         age |  -.0260364   .0045172    -5.76   0.000    -.0348899   -.0171829
        male |   .8217623   .1325189     6.20   0.000       .56203    1.081495
     college |  -.3631904   .1254201    -2.90   0.004    -.6090094   -.1173715
    fulltime |   .2482116   .1342755     1.85   0.065    -.0149637    .5113868
    parttime |  -.1903708   .1909451    -1.00   0.319    -.5646164    .1838748
      income |  -.0135058   .0438075    -0.31   0.758     -.099367    .0723554
socialstatus |  -.0511477   .0358906    -1.43   0.154    -.1214919    .0191965
        news |   .1943775   .0891642     2.18   0.029     .0196188    .3691361
     defense |   .6768203   .1147766     5.90   0.000     .4518623    .9017783
         ldp |   .2188291   .1561902     1.40   0.161     -.087298    .5249562
     noparty |  -.1352526   .1431443    -0.94   0.345    -.4158102    .1453051
     conserv |   .0961688   .0296718     3.24   0.001     .0380132    .1543244
       _cons |  -1.896814   .4054196    -4.68   0.000    -2.691422   -1.102206
------------------------------------------------------------------------------

. outreg2 using tableA3, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA3.tex
tableA3.xml
dir : seeout

. 
. seeout, lab
Hit Enter to continue. 

.                         
.                         
. //Appendix Table 4:  Using the Geographical Proximity Control
. 
. logit q100_1 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor if q100_1 > -1

Iteration 0:   log likelihood =  -1210.512  
Iteration 1:   log likelihood = -1085.2497  
Iteration 2:   log likelihood = -1085.1458  
Iteration 3:   log likelihood = -1085.1458  

Logistic regression                             Number of obs     =      1,760
                                                LR chi2(19)       =     250.73
                                                Prob > chi2       =     0.0000
Log likelihood = -1085.1458                     Pseudo R2         =     0.1036

------------------------------------------------------------------------------
      q100_1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .8911518   .1446094     6.16   0.000     .6077226    1.174581
  hist_japan |   .4032202   .1272485     3.17   0.002     .1538177    .6526227
hist_foreign |  -.3519454   .1275747    -2.76   0.006    -.6019872   -.1019036
 nationalism |   1.131152    .200371     5.65   0.000     .7384319    1.523872
    powerful |   .1942817   .1034635     1.88   0.060     -.008503    .3970664
    valuable |   .2333805   .1038473     2.25   0.025     .0298435    .4369174
         age |  -.0000985    .004169    -0.02   0.981    -.0082697    .0080726
        male |   .4934306   .1197831     4.12   0.000     .2586601    .7282012
     college |  -.0673878   .1153098    -0.58   0.559    -.2933908    .1586151
    fulltime |   .0047176    .126138     0.04   0.970    -.2425082    .2519435
    parttime |  -.0935392   .1646222    -0.57   0.570    -.4161929    .2291144
      income |   .0171447   .0408247     0.42   0.675    -.0628702    .0971597
socialstatus |  -.0774518   .0342924    -2.26   0.024    -.1446637   -.0102399
        news |   .1778689   .0842178     2.11   0.035      .012805    .3429328
     defense |   .4805301   .1104639     4.35   0.000     .2640248    .6970353
         ldp |   .1280952   .1509751     0.85   0.396    -.1678105    .4240009
     noparty |  -.0164959   .1293521    -0.13   0.899    -.2700215    .2370296
     conserv |   .0667634   .0280604     2.38   0.017     .0117661    .1217607
 mindist_nor |  -.3958601   .1931691    -2.05   0.040    -.7744646   -.0172556
       _cons |  -2.124065   .4013476    -5.29   0.000    -2.910692   -1.337439
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) re
> place
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_2 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_5 > -1

Iteration 0:   log likelihood = -1238.5433  
Iteration 1:   log likelihood = -1168.7842  
Iteration 2:   log likelihood = -1168.4844  
Iteration 3:   log likelihood = -1168.4843  

Logistic regression                             Number of obs     =      1,842
                                                LR chi2(19)       =     140.12
                                                Prob > chi2       =     0.0000
Log likelihood = -1168.4843                     Pseudo R2         =     0.0566

------------------------------------------------------------------------------
      q100_2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7824889   .1376943     5.68   0.000     .5126131    1.052365
  hist_japan |    .227404   .1226397     1.85   0.064    -.0129654    .4677734
hist_foreign |  -.1225189   .1222057    -1.00   0.316    -.3620376    .1169998
 nationalism |   .4616111   .1873442     2.46   0.014     .0944232     .828799
    powerful |   .0166306   .0994818     0.17   0.867    -.1783501    .2116113
    valuable |   .0811691   .0997669     0.81   0.416    -.1143704    .2767086
         age |  -.0205565   .0040227    -5.11   0.000    -.0284408   -.0126722
        male |   .3939189    .115047     3.42   0.001      .168431    .6194069
     college |  -.1443696   .1117666    -1.29   0.196    -.3634281    .0746889
    fulltime |  -.0246287   .1210208    -0.20   0.839     -.261825    .2125677
    parttime |   .0079725   .1588039     0.05   0.960    -.3032775    .3192225
      income |    .018159   .0391099     0.46   0.642     -.058495    .0948129
socialstatus |  -.0711671   .0324462    -2.19   0.028    -.1347605   -.0075736
        news |  -.0266922   .0790287    -0.34   0.736    -.1815856    .1282012
     defense |    .286539   .1062329     2.70   0.007     .0783264    .4947517
         ldp |   .2055711   .1451707     1.42   0.157    -.0789582    .4901005
     noparty |   .0418228   .1249971     0.33   0.738     -.203167    .2868126
     conserv |   .0666038   .0270567     2.46   0.014     .0135736     .119634
 mindist_nor |  -.1652938   .1840939    -0.90   0.369    -.5261112    .1955237
       _cons |  -.1457781   .3780818    -0.39   0.700    -.8868048    .5952486
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_5 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_7 > -1

Iteration 0:   log likelihood = -910.35689  
Iteration 1:   log likelihood = -815.18451  
Iteration 2:   log likelihood = -809.75035  
Iteration 3:   log likelihood = -809.74383  
Iteration 4:   log likelihood = -809.74383  

Logistic regression                             Number of obs     =      1,694
                                                LR chi2(19)       =     201.23
                                                Prob > chi2       =     0.0000
Log likelihood = -809.74383                     Pseudo R2         =     0.1105

------------------------------------------------------------------------------
      q100_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   -1.59737   .1441301   -11.08   0.000    -1.879859    -1.31488
  hist_japan |   -.373778   .1534361    -2.44   0.015    -.6745073   -.0730488
hist_foreign |  -.1450168   .1592666    -0.91   0.363    -.4571736      .16714
 nationalism |  -.1339316   .2314487    -0.58   0.563    -.5875628    .3196996
    powerful |   .1038576   .1247977     0.83   0.405    -.1407413    .3484566
    valuable |  -.3587403   .1261013    -2.84   0.004    -.6058944   -.1115862
         age |   .0160123   .0049934     3.21   0.001     .0062255    .0257991
        male |  -.3118891   .1463237    -2.13   0.033    -.5986783   -.0250999
     college |   .2263302   .1382397     1.64   0.102    -.0446145     .497275
    fulltime |  -.2083927   .1515639    -1.37   0.169    -.5054526    .0886671
    parttime |  -.0113141   .2029459    -0.06   0.956    -.4090807    .3864525
      income |   .0914475    .048524     1.88   0.059    -.0036577    .1865527
socialstatus |   -.003263   .0396055    -0.08   0.934    -.0808882    .0743623
        news |   .1183417   .0979245     1.21   0.227    -.0735867    .3102701
     defense |  -.3506031   .1297534    -2.70   0.007    -.6049152   -.0962911
         ldp |  -.3081691    .183898    -1.68   0.094    -.6686027    .0522644
     noparty |  -.2438117   .1619051    -1.51   0.132      -.56114    .0735165
     conserv |    -.01948   .0331522    -0.59   0.557    -.0844571     .045497
 mindist_nor |   -.233685   .2283161    -1.02   0.306    -.6811764    .2138064
       _cons |   1.386321   .4769406     2.91   0.004     .4515346    2.321107
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_6 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_6 > -1

Iteration 0:   log likelihood = -650.57089  
Iteration 1:   log likelihood = -621.73764  
Iteration 2:   log likelihood = -619.89057  
Iteration 3:   log likelihood = -619.88507  
Iteration 4:   log likelihood = -619.88507  

Logistic regression                             Number of obs     =      1,963
                                                LR chi2(19)       =      61.37
                                                Prob > chi2       =     0.0000
Log likelihood = -619.88507                     Pseudo R2         =     0.0472

------------------------------------------------------------------------------
      q100_6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -.0445405   .2143689    -0.21   0.835    -.4646959    .3756149
  hist_japan |   .3862139   .1927672     2.00   0.045     .0083971    .7640307
hist_foreign |   .0963242   .1813976     0.53   0.595    -.2592086     .451857
 nationalism |   .7720024   .2640328     2.92   0.003     .2545076    1.289497
    powerful |   .3941552   .1528841     2.58   0.010     .0945078    .6938026
    valuable |   .0213416   .1522364     0.14   0.889    -.2770363    .3197194
         age |   .0239191   .0060774     3.94   0.000     .0120075    .0358306
        male |  -.1146882   .1717986    -0.67   0.504    -.4514071    .2220308
     college |    .114178   .1705292     0.67   0.503    -.2200531    .4484091
    fulltime |  -.0106788   .1773948    -0.06   0.952    -.3583662    .3370086
    parttime |   .4761372   .2731184     1.74   0.081    -.0591651     1.01144
      income |   .0532796     .05827     0.91   0.361    -.0609275    .1674866
socialstatus |  -.0817621   .0487567    -1.68   0.094    -.1773234    .0137992
        news |   .2498781   .1167802     2.14   0.032     .0209931     .478763
     defense |  -.1796451   .1617554    -1.11   0.267      -.49668    .1373897
         ldp |   .3633358    .218107     1.67   0.096    -.0641461    .7908177
     noparty |   .3158239   .1846275     1.71   0.087    -.0460394    .6776873
     conserv |  -.0395258   .0408829    -0.97   0.334    -.1196549    .0406033
 mindist_nor |   .4865764   .2818618     1.73   0.084    -.0658627    1.039015
       _cons |  -.5820908   .5688456    -1.02   0.306    -1.697008     .532826
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_7 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_2 > -1

Iteration 0:   log likelihood = -1253.6376  
Iteration 1:   log likelihood = -1145.4166  
Iteration 2:   log likelihood = -1145.1714  
Iteration 3:   log likelihood = -1145.1713  
Iteration 4:   log likelihood = -1145.1713  

Logistic regression                             Number of obs     =      1,809
                                                LR chi2(19)       =     216.93
                                                Prob > chi2       =     0.0000
Log likelihood = -1145.1713                     Pseudo R2         =     0.0865

------------------------------------------------------------------------------
      q100_7 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |  -1.412736   .1619952    -8.72   0.000    -1.730241   -1.095231
  hist_japan |  -.5997735   .1251746    -4.79   0.000    -.8451112   -.3544359
hist_foreign |   -.089331   .1226565    -0.73   0.466    -.3297334    .1510713
 nationalism |  -.2599126   .1893799    -1.37   0.170    -.6310905    .1112653
    powerful |  -.0040174   .1003905    -0.04   0.968    -.2007792    .1927444
    valuable |  -.0079238   .1009545    -0.08   0.937     -.205791    .1899434
         age |   .0089597   .0040071     2.24   0.025     .0011059    .0168135
        male |  -.1308206   .1157479    -1.13   0.258    -.3576824    .0960411
     college |   .2494401   .1124037     2.22   0.026     .0291329    .4697474
    fulltime |   -.096301   .1208203    -0.80   0.425    -.3331043    .1405024
    parttime |   .0008529   .1602824     0.01   0.996    -.3132947    .3150005
      income |  -.0380027   .0389633    -0.98   0.329    -.1143693     .038364
socialstatus |   .0640842   .0326901     1.96   0.050     .0000128    .1281556
        news |  -.0197505   .0798035    -0.25   0.805    -.1761625    .1366615
     defense |  -.4817466   .1076342    -4.48   0.000    -.6927057   -.2707875
         ldp |  -.2044404   .1473886    -1.39   0.165    -.4933168    .0844361
     noparty |  -.1405748   .1255254    -1.12   0.263       -.3866    .1054505
     conserv |  -.0814993   .0274628    -2.97   0.003    -.1353255   -.0276732
 mindist_nor |  -.1773361   .1866916    -0.95   0.342    -.5432449    .1885727
       _cons |   .8822594    .386921     2.28   0.023     .1239081    1.640611
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_3 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_3 > -1

Iteration 0:   log likelihood = -1284.0812  
Iteration 1:   log likelihood = -1136.3694  
Iteration 2:   log likelihood = -1136.2179  
Iteration 3:   log likelihood = -1136.2178  

Logistic regression                             Number of obs     =      1,859
                                                LR chi2(19)       =     295.73
                                                Prob > chi2       =     0.0000
Log likelihood = -1136.2178                     Pseudo R2         =     0.1152

------------------------------------------------------------------------------
      q100_3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7466575   .1420852     5.25   0.000     .4681757    1.025139
  hist_japan |   .0946059   .1252884     0.76   0.450    -.1509549    .3401666
hist_foreign |   -.399857   .1248791    -3.20   0.001    -.6446156   -.1550984
 nationalism |   1.140395   .1928454     5.91   0.000     .7624252    1.518365
    powerful |   .2431407   .1014315     2.40   0.017     .0443386    .4419428
    valuable |    .053372   .1014803     0.53   0.599    -.1455258    .2522697
         age |   -.027165   .0040995    -6.63   0.000    -.0351998   -.0191301
        male |   .3393871   .1171458     2.90   0.004     .1097856    .5689886
     college |  -.1069826   .1131025    -0.95   0.344    -.3286594    .1146943
    fulltime |   .0858176   .1229354     0.70   0.485    -.1551314    .3267666
    parttime |   .0932653   .1607568     0.58   0.562    -.2218122    .4083427
      income |    .043295   .0393959     1.10   0.272    -.0339195    .1205095
socialstatus |  -.0835507   .0330071    -2.53   0.011    -.1482433   -.0188581
        news |   .1275639   .0823901     1.55   0.122    -.0339178    .2890456
     defense |   .5705675   .1077139     5.30   0.000     .3594522    .7816828
         ldp |   .5629233   .1460931     3.85   0.000      .276586    .8492606
     noparty |   .0369964   .1269665     0.29   0.771    -.2118534    .2858462
     conserv |   .0753805   .0272683     2.76   0.006     .0219356    .1288254
 mindist_nor |  -.0139652   .1876419    -0.07   0.941    -.3817366    .3538061
       _cons |  -.8495977   .3935771    -2.16   0.031    -1.620995   -.0782007
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. logit q100_4 hardcore hist_japan hist_foreign nationalism powerful valuable age male college fulltime parttime income socialstat
> us news defense ldp noparty conserv mindist_nor  if q100_4 > -1

Iteration 0:   log likelihood = -1106.2905  
Iteration 1:   log likelihood = -965.87199  
Iteration 2:   log likelihood = -960.18553  
Iteration 3:   log likelihood = -960.17116  
Iteration 4:   log likelihood = -960.17116  

Logistic regression                             Number of obs     =      1,920
                                                LR chi2(19)       =     292.24
                                                Prob > chi2       =     0.0000
Log likelihood = -960.17116                     Pseudo R2         =     0.1321

------------------------------------------------------------------------------
      q100_4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hardcore |   .7726487   .1428532     5.41   0.000     .4926616    1.052636
  hist_japan |  -.1156615   .1393604    -0.83   0.407    -.3888029    .1574798
hist_foreign |  -.2333338   .1398316    -1.67   0.095    -.5073986    .0407311
 nationalism |   1.132472   .2238484     5.06   0.000     .6937367    1.571206
    powerful |   -.059886   .1133606    -0.53   0.597    -.2820688    .1622967
    valuable |   .0098889   .1135425     0.09   0.931    -.2126504    .2324282
         age |  -.0276312   .0045587    -6.06   0.000    -.0365661   -.0186964
        male |   .7677697   .1350014     5.69   0.000     .5031718    1.032368
     college |  -.3704536   .1270452    -2.92   0.004    -.6194577   -.1214495
    fulltime |     .23745   .1366194     1.74   0.082    -.0303192    .5052191
    parttime |  -.2537879     .19497    -1.30   0.193     -.635922    .1283462
      income |  -.0308289   .0446744    -0.69   0.490    -.1183891    .0567312
socialstatus |   -.053211    .036365    -1.46   0.143    -.1244851    .0180632
        news |   .1734994    .092373     1.88   0.060    -.0075484    .3545473
     defense |   .6418009   .1172282     5.47   0.000     .4120379     .871564
         ldp |    .252023   .1581232     1.59   0.111    -.0578928    .5619388
     noparty |  -.1498598   .1460938    -1.03   0.305    -.4361984    .1364787
     conserv |   .0967158   .0300995     3.21   0.001     .0377218    .1557098
 mindist_nor |   -.060029   .2084319    -0.29   0.773    -.4685481    .3484901
       _cons |  -1.794881   .4338003    -4.14   0.000    -2.645114   -.9446478
------------------------------------------------------------------------------

. outreg2 using tableA4, lab excel tex 2aster addstat(Pseudo R-squared, `e(r2_p)', LR $\chi^2$, e(chi2), Prob < $\chi^2$, e(p)) ap
> pend
tableA4.tex
tableA4.xml
dir : seeout

. 
. seeout, lab
Hit Enter to continue. 

. 
. //Appendix Table 1
. 
. tabstat hardcore hardcore4 nationalism1-nationalism5 nationalism age male college fulltime parttime income socialstatus news def
> ense ldp noparty conserv,  stat(n mean median sd min max) format(%9.2f) col(stat) long

    variable |         N      mean       p50        sd       min       max
-------------+------------------------------------------------------------
    hardcore |   2389.00      0.16      0.00      0.37      0.00      1.00
   hardcore4 |   2621.00     -0.01      0.00      0.75     -1.18      0.99
nationalism1 |   2621.00      0.81      1.00      0.39      0.00      1.00
nationalism2 |   2621.00      0.75      1.00      0.43      0.00      1.00
nationalism3 |   2621.00      0.48      0.00      0.50      0.00      1.00
nationalism4 |   2621.00      0.76      1.00      0.43      0.00      1.00
nationalism5 |   2621.00      0.29      0.00      0.45      0.00      1.00
 nationalism |   2621.00      0.62      0.60      0.31      0.00      1.00
         age |   2621.00     47.04     46.00     13.57     21.00     69.00
        male |   2621.00      0.52      1.00      0.50      0.00      1.00
     college |   2621.00      0.57      1.00      0.50      0.00      1.00
    fulltime |   2621.00      0.46      0.00      0.50      0.00      1.00
    parttime |   2621.00      0.15      0.00      0.36      0.00      1.00
      income |   2611.00      2.90      3.00      1.52      1.00      5.00
socialstatus |   2621.00      4.82      5.00      1.83      0.00     10.00
        news |   2621.00      2.97      3.00      0.75      1.00      4.00
     defense |   2621.00      0.38      0.00      0.49      0.00      1.00
         ldp |   2621.00      0.26      0.00      0.44      0.00      1.00
     noparty |   2621.00      0.49      0.00      0.50      0.00      1.00
     conserv |   2371.00      5.49      5.00      1.98      0.00     10.00
--------------------------------------------------------------------------

. 
. 
. 
. ***END***                               
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.                         
.         
. 
end of do-file

. log close
      name:  <unnamed>
       log:  D:\Dropbox\2016 Japan project\Japan Main Survey\replication\replication.log
  log type:  text
 closed on:  16 Feb 2022, 13:27:58
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